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This paper suggests a novel fusion approach of combining Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance multi-criteria decision-making for energy management. In this way, by integrating the two powerful methodologies, the fusion approach is enabled that allows for dealing with the complex and highly dynamic character of energy management decisions for which there is required the careful consideration of many conflicting criteria. The method uses A to derive weightings for each decision criterion via AHP based on expert judgments, ensuring all relevant factors are systematically considered proportionately. Subsequently, TOPSIS is applied to evaluate and rank the alternatives such that the most effective energy solutions close to the ideal solution are identified. This integration of AHP with TOPSIS leads to comprehensive analysis that draws both the strengths of these techniques and provides a powerful tool to make informed and balanced decisions in the energy sector. The effectiveness of this fusion method, when applied, could then lead to the attainment of subtler findings and dependable suggestions, making it a beneficial contribution to this field of energy management. [ABSTRACT FROM AUTHOR]

In order to improve the utilization rate of internet data under heterogeneous distribution, increase the diversified usage functions and data transmission rate of the internet, and reduce the running time of the internet, it is necessary to mine internet data under heterogeneous distribution. The author proposes an ontology based optimization method for internet data mining under heterogeneous distribution; This method first preprocesses and selects data features from internet data under heterogeneous distribution, and uses a feature selection decision system to select features from the mining data. Based on this, information entropy is used to filter internet data under heterogeneous distribution. During the filtering process, the theoretical values filtered by information entropy are reduced to obtain the optimal data filtering value, finally, based on the various data information obtained in the preprocessing, the iterative calculation results of the information gain value in the decision tree generation algorithm are used to high-precision mine internet data under heterogeneous distribution; The simulation experimental results demonstrate that the proposed method improves the flexibility of internet data operations under heterogeneous distribution, increases the recyclability of internet data, and makes internet operations under heterogeneous distribution more concise and efficient, providing a strong basis for research and development in this field. [ABSTRACT FROM AUTHOR]

Considering the high importance of the optimization problem, this study evaluated mathematical programming models by considering various methods of estimating model coefficients. Correct and accurate data must be entered into the model to get accurate and robust result from the model. Most input data to the presented model are technical and objective function coefficients. Therefore, it is necessary to determine the information related to these coefficients with the utmost precision and, as much as possible, to develop a suitable scientific method to estimate the value of these coefficients [5]. Finding the best method for estimating the coefficients of mathematical programming models can significantly optimize the final values of the variables extracted from the mathematical programming model. For this reason, it is essential to study the methods used so far in this field and examine their advantages and disadvantages. This review study investigated various methods of estimating technical coefficients of mathematical planning models in the conditions of possible decision-making and uncertainty after reviewing 117 articles published between 1955 and 2022. These methods include fuzzy methods, statistical methods, and data analysis methods. Statistical methods such as regression methods, time series methods, exponential smoothing, and linear non-linear and non-parametric, machine learning and data mining methods were investigated in this article. The methods of data-driven analysis explained in this article can be referred to as decision trees, random forests and the Lasso methods. After evaluating and comparing these methods, suggestions for choosing the best method were provided. [ABSTRACT FROM AUTHOR]

As a result of the uneven development of certain territories, it is more feasible and effective to tackle the practical issues of labor market regulation at the regional level. This ensures sufficient regulation of the system. Since it is necessary to properly account for the regional differences in practice, it is required that these issues be methodologically justified. Therefore, the aim of this paper is to investigate regional labor markets based on indicators of the socio-economic development of regions using the data mining methods. The current study has clustered regions of Ukraine on the basis of the level of their socio-economic development using data mining methods, in particular Kohonen maps and the k-means methods. One of the most critical stages in the assessment of Ukraine’s regions in terms of socio-economic development by using data mining methods is to determine the information base, criteria of evaluation, and a list of estimates. The data mining methods have gained much popularity in the assessing regional differentiation. The conducted analysis based on data mining methods included the use of the Deductor software, which includes the following analytical algorithms: neural networks, Kohonen’s self-organizing maps, autocorrelation and regression, associative rules, decision trees. For our study, we used the cluster analysis method based on Kohonen’s self-organizing maps as one of the most popular and frequently used methods for solving problems of the regional economy and assessing the differentiation of regions. In the context of our task, the result of cluster analysis is clusters of regions, united by indices of socioeconomic development. The main aspects of the socio-economic and demographic development of the regions are characterized by a set of statistical indicators related to four blocks of key factors: 1. Assessment of the demographic situation in a region. 2. Assessment of the social situation in a region. 3. Assessment of the economic situation in a region. 4. Assessment of the organizational environment in a region. The study, by no means, claims to detect all the dependences in the labor market related to all the above-mentioned factors. Based on public data, given in the statistical yearbook “Ukraine in Figures” (2020), by using mathematical methods (correlation-regression and cluster analysis), we obtained two groups of factors that characterize different aspects of the socio-economic and demographic development. The ranking of the regions by the level of extensive and intensive development shows that the development of the regions in Ukraine mainly proceeds in the extensive path of development. Almost all regions of Ukraine demonstrate a low level of intensive development. The integrated coefficient of intensive development for many territories is far from a maximum value; there are well distinguishable and huge discrepancies in the levels of the regions’ intensive development. Such a gap between the natural and human resource potentials, on the one hand, and the level of the development of economic activity and its territorial organization within the regions, on the other hand, leads to investment unattractiveness of some territories. Thus, the estimation of the country’s regions based on the level of their socio-economic development testifies to the dominance of extensive factors in the development of most regions in Ukraine. Common areas of the policy, conducted in the labor market, for all groups of regions are the measures to conduct an active policy (promoting self-employment and small businesses; the creation of new jobs; vocational training and retraining of unemployed people; public works; improvement of employment services, etc.).

Background: Hepatitis C virus (HCV) has a high prevalence worldwide, and the progression of the disease can cause irreversible damage to severe liver damage or even death. Therefore, developing prediction models using machine learning techniques is beneficial. This study was conducted to classify suspected patients with HCV infection using different classification models. Methods: The study was conducted using a dataset derived from the University of California, Irvine (UCI) Machine Learning Repository. Since the HCV dataset was imbalanced, the synthetic minority oversampling technique (SMOTE) was applied to balance the dataset. After cleaning the dataset, it was divided into training and test data for developing six classification models. These six algorithms included the support vector machine (SVM), Gaussian Naïve Bayes (NB), decision tree (DT), random forest (RF), logistic regression (LR), and K-nearest neighbors (KNN) algorithm. The Python programming language was used to develop the classifiers. Receiver operating characteristic curve analysis and other metrics were used to evaluate the performance of the proposed models. Results: After the evaluation of the models using different metrics, the RF classifier had the best performance among the six methods. The accuracy of the RF classifier was 97.29%. Accordingly, the area under the curve (AUC) for LR, KNN, DT, SVM, Gaussian NB, and RF models were 0.921, 0.963, 0.953, 0.972, 0.896, and 0.998, respectively, RF showing the best predictive performance. Conclusion: Various machine learning techniques for classifying healthy and unhealthy patients were used in this study. Additionally, the developed models might identify the stage of HCV based on trained data.

Structured reporting (SR) not only offers advantages regarding report quality but, as an IT-based method, also the opportunity to aggregate and analyze large, highly structured datasets (data mining). In this study, a data mining algorithm was used to calculate epidemiological data and in-hospital prevalence statistics of pulmonary embolism (PE) by analyzing structured CT reports.All structured reports for PE CT scans from the last 5 years (n = 2790) were extracted from the SR database and analyzed. The prevalence of PE was calculated for the entire cohort and stratified by referral type and clinical referrer. Distributions of the manifestation of PEs (central, lobar, segmental, subsegmental, as well as left-sided, right-sided, bilateral) were calculated, and the occurrence of right heart strain was correlated with the manifestation.The prevalence of PE in the entire cohort was 24% (n = 678). The median age of PE patients was 71 years (IQR 58-80), and the sex distribution was 1.2/1 (M/F). Outpatients showed a lower prevalence of 23% compared to patients from regular wards (27%) and intensive care units (30%). Surgically referred patients had a higher prevalence than patients from internal medicine (34% vs. 22%). Patients with central and bilateral PEs had a significantly higher occurrence of right heart strain compared to patients with peripheral and unilateral embolisms.Data mining of structured reports is a simple method for obtaining prevalence statistics, epidemiological data, and the distribution of disease characteristics, as demonstrated by the PE use case. The generated data can be helpful for multiple purposes, such as for internal clinical quality assurance and scientific analyses. To benefit from this, consistent use of SR is required and is therefore recommended. · SR-based data mining allows simple epidemiologic analyses for PE.. · The prevalence of PE differs between outpatients and inpatients.. · Central and bilateral PEs have an increased risk of right heart strain.. · Jorg T, Halfmann MC, Graafen D et al. Structured reporting for efficient epidemiological and in-hospital prevalence analysis of pulmonary embolisms. Rofo 2025; 197: 186-195., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background: Linking registered clinical trials with their published results continues to be a challenge. A variety of natural language processing (NLP)-based and machine learning-based models have been developed to assist users in identifying these connections. To date, however, no system has attempted to detect mentions of registry numbers within the full-text of articles., Methods: Articles from the PubMed Central full-text Open Access dataset were scanned for mentions of ClinicalTrials.gov and international clinical trial registry identifiers. We analyzed the distribution of trial registry numbers within sections of the articles and characterized their publication type indexing and other metrics., Results: Registry numbers mentioned in article metadata (e.g., the abstract) or in the Methods section of full-text are highly predictive of clinical trial articles. When a clinical trial article mentioned ClinicalTrials.gov identifier numbers (NCT) only in the Methods section, in every case examined, it was reporting clinical outcomes from that registered trial, and thus can reliably be used to link that trial to that publication. Conversely, registry numbers mentioned in Tables arise almost entirely from reviews (including systematic reviews and meta-analyses). Registry numbers mentioned in other full-text sections have relatively little predictive value for linking trials to their publications. Clinical trial articles that mention CONSORT or SPIRIT guidelines have a higher rate of mentioning registry numbers in article metadata, and hence are more easily linked to their underlying trials, than articles overall., Conclusions: The appearance and location of trial registry numbers within the full-text of biomedical articles provide valuable features for connecting clinical trials to their publications. They also potentially provide information to assist automated tools in assigning publication types to articles., Competing Interests: Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests., (© 2025. The Author(s).)

Background: Biomedical text mining is a technique that extracts essential information from scientific articles using named entity recognition (NER). Traditional NER methods rely on dictionaries, rules, or curated corpora, which may not always be accessible. To overcome these challenges, deep learning (DL) methods have emerged. However, DL-based NER methods may need help identifying long-distance relationships within text and require significant annotated datasets., Results: This research has proposed a novel model to address the challenges in natural language processing. The Improved Green anaconda-assisted Bi-GRU based Hierarchical ResNet BNER model (IGa-BiHR BNERM) is the model. IGa-BiHR BNERM model has shown promising results in accurately identifying named entities. The MACCROBAT dataset was obtained from Kaggle and underwent several pre-processing steps such as Stop Word Filtering, WordNet processing, Removal of non-alphanumeric characters, stemming Segmentation, and Tokenization, which is standardized and improves its quality. The pre-processed text was fed into a feature extraction model like the Robustly Optimized BERT -Whole Word Masking model. This model provides word embeddings with semantic information. Then, the BNER process utilized an Improved Green Anaconda-assisted Bi-GRU-based Hierarchical ResNet BNER model (IGa-BiHR BNERM)., Conclusion: To improve the training phase of the IGa-BiHR BNERM, the Improved Green Anaconda Optimization technique was used to select optimal weight parameter coefficients for training the model parameters. After the model was tested using the MACCROBAT dataset, it outperformed previous models with a tremendous accuracy rate of 99.11%. This model effectively and accurately identifies biomedical names within the text, significantly advancing this field., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples. However, this approach can sometimes result in the misinterpretation of amebiasis as other gastroenteritis (GE) conditions. The goal of the work is to produce a machine learning (ML) model that uses laboratory findings and demographic information to automatically predict amebiasis., Method: Data extracted from Jordanian electronic medical records (EMR) between 2020 and 2022 comprised 763 amebic cases and 314 nonamebic cases. Patient demographics, clinical signs, microscopic diagnoses, and leukocyte counts were used to train eight decision tree algorithms and compare their accuracy of predictions. Feature ranking and correlation methods were implemented to enhance the accuracy of classifying amebiasis from other conditions., Results: The primary dependent variables distinguishing amebiasis include the percentage of neutrophils, mucus presence, and the counts of red blood cells (RBCs) and white blood cells (WBCs) in stool samples. Prediction accuracy and precision ranged from 92% to 94.6% when employing decision tree classifiers including decision tree (DT), random forest (RF), XGBoost, AdaBoost, and gradient boosting (GB). However, the optimized RF model demonstrated an area under the curve (AUC) of 98% for detecting amebiasis from laboratory data, utilizing only 300 estimators with a max depth of 20. This study highlights that amebiasis is a significant health concern in Jordan, responsible for 17.22% of all gastroenteritis episodes in this study. Male sex and age were associated with higher incidence of amebiasis (P = 0.014), with over 25% of cases occurring in infants and toddlers., Conclusions: The application of ML to EMR can accurately predict amebiasis. This finding significantly contributes to the emerging use of ML as a decision support system in parasitic disease diagnosis., Competing Interests: Declarations. Ethics approval and consent to participate: This study has been approved by the scientific and administration committee of scientific research at Al-Balqa Applied University and Jordan’s Al-Hussein/Salt Hospital. The material is the authors’ original work that has not been previously published elsewhere. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Reverse transcription quantitative real-time PCR (RT-qPCR) is esteemed for its precision and reliability, positioning it as the standard for evaluating gene expression. Selecting suitable reference genes is crucial for acquiring accurate data on target gene expression. However, identifying appropriate reference genes for specific rice tissues or growth conditions has been a challenge. To overcome this, we introduce the Rice Reference Genes (RRG) tool ( https://www.rrgenes.com/ ), which assists researchers in selecting reference genes for diverse experimental conditions in rice. This tool utilizes 4404 rice-derived RNA-seq datasets, categorized by five tissue types-leaf, root, seedling, panicle, and seed-and seven stress conditions (cold, disease, drought, heat, hormone, metal, and salt), along with corresponding control groups (mock). In this research, we employed the RRG web-based tool to identify candidate reference genes in rice leaves, roots, and seedlings exposed to salt and drought stress. These candidates were rigorously tested against conventionally established reference genes, confirming their accuracy and reliability. The RRG tool is designed to be user-friendly, allowing even those with limited experience to efficiently select optimal reference genes in rice with ease., (© The Author(s) 2024. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site–for further information please contact journals.permissions@oup.com.)

Background: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting their utility in research. This study aims to assess whether an "out of the box" implementation of open-source large language models (LLMs) without any fine-tuning can accurately extract social determinants of health (SDoH) data from free-text clinical notes., Methods: We conducted a cross-sectional study using EHR data from the Mass General Brigham (MGB) system, analyzing free-text notes for SDoH information. We selected a random sample of 200 patients and manually labeled nine SDoH aspects. Eight advanced open-source LLMs were evaluated against a baseline pattern-matching model. Two human reviewers provided the manual labels, achieving 93% inter-annotator agreement. LLM performance was assessed using accuracy metrics for overall, mentioned, and non-mentioned SDoH, and macro F1 scores., Results: LLMs outperformed the baseline pattern-matching approach, particularly for explicitly mentioned SDoH, achieving up to 40% higher Accuracy mentioned . openchat_3.5 was the best-performing model, surpassing the baseline in overall accuracy across all nine SDoH aspects. The refined pipeline with prompt engineering reduced hallucinations and improved accuracy., Conclusions: Open-source LLMs are effective and scalable tools for extracting SDoH from unstructured EHRs, surpassing traditional pattern-matching methods. Further refinement and domain-specific training could enhance their utility in clinical research and predictive analytics, improving healthcare outcomes and addressing health disparities., Competing Interests: Declarations. Ethics approval and consent to participate: Mass General Brigham (MGB) Institutional Review Board approved the study protocol (2020P001486). Consent to participate was not deemed required for this observational investigation. Consent for publication: Not applicable. Competing interests: Dr. Desai reports serving as Principal Investigator on investigator-initiated grants to the Brigham and Women’s Hospital from Novartis, Vertex, and Bayer on unrelated projects.Other authors do not have any competing interests to disclose. Other authors do not have any competing interests to disclose., (© 2025. The Author(s).)

Background and Method: This study set out to identify the factors and combinations of factors associated with the individual's premature death, using data from the Finnish Longitudinal Study on Ageing Municipal Employees (FLAME) which involved 6,257 participants over a 29-year follow-up period. Exact dates of death were obtained from the Finnish population register. Premature death was defined as a death occurring earlier than the age- and sex-specific actuarial life expectancy indicated by life tables for 1981, as the baseline, with the threshold period of nine months. Explanatory variables encompassed sociodemographic characteristics, health and functioning, health behaviors, subjective experiences, working conditions, and work abilities. Data were mined using the General Unary Hypothesis Automaton (GUHA) method, implemented with LISp-Miner software. GUHA involves an active dialogue between the user and the LISp-Miner software, with parameters tailored to the data and user interests. The parameters used are not absolute but depend on the data to be mined and the user's interests., Results: Over the follow-up period, 2,196 deaths were recorded, of which 70.4% were premature. Seven single factors and 67 sets of criteria (paths) were statistically significantly associated with premature mortality, passing the one-sided Fisher test. Single predicates of premature death included smoking, consuming alcohol a few times a month or once a week, poor self-rated fitness, incompetence to work and poor assured workability in two years' time, and diseases causing work disability. Notably, most of the factors selected as single predicates of premature mortality did not appear in the multi-predicate paths. Factors appearing in the paths were smoking more than 20 cigarettes a day, symptoms that impaired functioning, past smoking, absence of musculoskeletal diseases, poor self-rated health, having pain, male sex, being married, use of medication, more physical strain compared to others, and high life satisfaction, intention to retire due to reduced work ability caused by diseases and demanding work. Sex-specific analysis revealed similar findings., Conclusion: The findings indicate that associations between single predictors and premature mortality should be interpreted with caution, even when adjusted for a limited number of other factors. This highlights the complexity of premature mortality and the need for comprehensive models considering multiple interacting factors., Competing Interests: Declarations. Ethics approval and consent to participate: The FLAME study was approved by the Ethics Committee of the Finnish Institute of Occupational Health, and ethical clearance for the register linkage was provided by the National Data Protection Ombudsman. All participants provided written informed consent. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

One of the principal goals of Precision Medicine is to stratify patients by accounting for individual variability. However, extracting meaningful information from Real-World Data, such as Electronic Health Records, still remains challenging due to methodological and computational issues. A Dynamic Time Warping-based unsupervised-clustering methodology is presented in this paper for the clustering of patient trajectories of multi-modal health data on the basis of shared temporal characteristics. Building on an earlier methodology, a new dimension of time-varying clinical and imaging features is incorporated, through an adapted cost-minimization algorithm for clustering on different, possibly overlapping, feature subsets. The model disease chosen is Huntington's disease (HD), characterized by progressive neurodegeneration. From a wide range of examined user-defined parameters, four case examples are highlighted to demonstrate the identified temporal patterns in multi-modal HD trajectories and to study how these differ due to the combined effects of feature weights and granularity threshold. For each identified cluster, polynomial fits that describe the time behavior of the assessed features are provided for an informative comparison, together with their averaged values. The proposed data-mining methodology permits the stratification of distinct time patterns of multi-modal health data in individuals that share a diagnosis, by employing user-customized criteria beyond the current clinical practice. Overall, this work bears implications for better analysis of individual variability in disease progression, opening doors to personalized preventative, diagnostic and therapeutic strategies., Competing Interests: Declarations. Competing interests: A. G. declares being an employee of AstraZeneca LLC and may hold stock. Ethics Approval: All methods were carried out in accordance with the Helsinki Declaration of 1975. The study was approved by the ethics committee of Bellvitge Hospital. All participants provided written informed consent to participate in the study., (© 2025. The Author(s).)

Background: Health misinformation undermines responses to health crises, with social media amplifying the issue. Although organizations work to correct misinformation, challenges persist due to reasons such as the difficulty of effectively sharing corrections and information being overwhelming. At the same time, social media offers valuable interactive data, enabling researchers to analyze user engagement with health misinformation corrections and refine content design strategies., Objective: This study aimed to identify the attributes of correction posts and user engagement and investigate (1) the trend of user engagement with health misinformation correction during 3 years of the COVID-19 pandemic; (2) the relationship between post attributes and user engagement in sharing and reactions; and (3) the content generated by user comments serving as additional information attached to the post, affecting user engagement in sharing and reactions., Methods: Data were collected from the Facebook pages of a fact-checking organization and a health agency from January 2020 to December 2022. A total of 1424 posts and 67,378 corresponding comments were analyzed. The posts were manually annotated by developing a research framework based on the fuzzy-trace theory, categorizing information into "gist" and "verbatim" representations. Three types of gist representations were examined: risk (risks associated with misinformation), awareness (awareness of misinformation), and value (value in health promotion). Furthermore, 3 types of verbatim representations were identified: numeric (numeric and statistical bases for correction), authority (authority from experts, scholars, or institutions), and facts (facts with varying levels of detail). The basic metrics of user engagement included shares, reactions, and comments as the primary dependent variables. Moreover, this study examined user comments and classified engagement as cognitive (knowledge-based, critical, and bias-based) or emotional (positive, negative, and neutral). Statistical analyses were performed to explore the impact of post attributes on user engagement., Results: On the basis of the results of the regression analysis, risk (β=.07; P=.001), awareness (β=.09; P<.001), and facts (β=.14; P<.001) predicted higher shares; awareness (β=.07; P=.001) and facts (β=.24; P<.001) increased reactions; and awareness (β=.06; P=.005), numeric representations (β=.06; P=.02), and facts (β=.19; P<.001) increased comments. All 3 gist representations significantly predicted shares (risk: β=.08; P<.001, awareness: β=.08; P<.001, and value: β=.06; P<.001) and reactions (risk: β=.04; P=.007, awareness: β=.06; P<.001, and value: β=.05; P<.001) when considering comment content. In addition, comments with bias-based engagement (β=-.11; P=.001) negatively predicted shares. Generally, posts providing gist attributes, especially awareness of misinformation, were beneficial for user engagement in misinformation correction., Conclusions: This study enriches the theoretical understanding of the relationship between post attributes and user engagement within web-based communication efforts to correct health misinformation. These findings provide a foundation for designing more effective content approaches to combat misinformation and strengthen public health communication., (©Hsin-Yu Kuo, Su-Yen Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.01.2025.)

Premature ventricular contraction (PVC) is characterized by early repolarization of the myocardium originating from Purkinje fibers. PVC may occur in individuals who are otherwise healthy. However, it may be associated with some pathological conditions. In this research the association between hematological factors and PVC was studied. In this study, 9,035 participants were enrolled in the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. The association of hematological factors with PVC was evaluated using different machine learning (ML) algorithms, including logistic regression (LR), C5.0, and boosting decision tree (DT). The dataset was divided into training and test, and each model's performance was appraised on the test dataset. All data analyses used SPSS version 26 and SPSS Modeler 10. The results show that the Boosting DT was the most effective algorithm. Boosting DT had an accuracy of 98.13% and 96.92% for males and females respectively. According to the models, RDW and PLT were the most significant hematological factors for both males and females. WBC, PDW, and HCT for males and RBC, MCV, and MXD for females were also important. Some hematological factors associated with PVC were found using ML models. Further studies are needed to confirm these results in other populations, considering the novelty of the exploration of the relationship between hematological parameters and PVC., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: All experiments were performed by the declaration of Helsinki and Mashhad University of Medical Sciences ethical guidelines and regulations. The research protocol was approved by the School of Medicine, Mashhad University of Medical Sciences, Biomedical Research Ethics Committee (IR.MUMS.MEDICAL.REC.1399.783). Also, informed consent was obtained from all subjects or their legal guardians., (© 2025. The Author(s).)

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available. This work reports on two compact sensor system designs, both reaching the FDA accuracy benchmark. Each design commonly comprises a mid-infrared QCL for emission, a multiple attenuation total reflection prism (MATR) for data acquisition, and a computer-controlled infrared detector for data analysis. The first design translates the comb-like signals into conventional spectra, and then data-mines the resultant spectra to yield blood glucose concentrations. When a pressure actuator is employed to press the patient's hypothenar against the MATR, the sensor accuracy is considered to reach the FDA accuracy benchmark. The second design abandons the data processing step of translating combs-to-spectra and directly data-mines the "first-hand" comb signal. Beyond increasing the measurement accuracy to the FDA accuracy benchmark, even without a pressure actuator, direct comb data-mining upgrades the sensor system with speed and data integrity, which can impact the healthcare of diabetic patients. Specifically, the sensor performance is validated with 492 glucose absorption scans in the time domain, each with 20 million datapoints measured from four subjects with glucose concentrations of 3.9-7.9 mM. The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. These concentrations are correlated with those measured with a standard finger-pricking glucometer. An accuracy of 99.6% is confirmed from the 164 measurements with errors not more than 15% from the reference of the standard glucometer.

Cancer, as a shocking disease, is one of the most common malignant tumors among women, posing a huge threat to the physical health and safety of women worldwide. With the continuous development of science and technology, more and more high and new technologies are involved in the diagnosis and prediction of breast cancer. In recent years, intelligent medical assistants supported by data mining and machine learning algorithms have provided necessary support for doctors' diagnosis. This study proposes an improved LightGBM hybrid integration model. Introducing gradient harmonic loss and cross entropy loss to enhance the model's attention to minority classes in the dataset and alleviate the impact of data imbalance on diagnostic results. Designing whale optimization algorithm to improve LightGBM to achieve iterative optimization of hyperparameters, and enhance the overall performance of the model. Proposing Jacobian regularization method to denoise LightGBM to solve the problem of model sensitivity to noise. Developing the LightGBM hybrid integration model to ensure the accuracy and stability of model diagnosis on diverse and imbalanced datasets. The effectiveness of the proposed method has been comprehensively compared and verified through the dataset in the UCI machine learning repository, and the results show that the proposed method has achieved good diagnostic performance in all indicators. The hybrid integration model proposed in this paper can provide effective auxiliary support for doctors to diagnose breast cancer., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Consent for publication: Freely and informed consent was obtained from all authors to participate in the study., (© 2025. The Author(s).)

The rapid increase of novel per- and polyfluoroalkyl substances (PFAS) raises concerns, while their identification remains challenging. Here, we develop a two-layer homolog network approach for PFAS nontarget screening using mass spectrometry. The first layer constructs networks between homologs, with evaluation showing that it filters 94% of false candidates. The second layer builds a network between classes to expedite the identification of PFAS. We detected 94 PFAS in twelve waterproof products and two related industrial sludges, including 36 novel PFAS not previously reported in any sample. A local dataset is constructed for retrospective analysis by re-analyzing our previous samples, revealing fifteen novel PFAS in samples collected in 2005. The retrieval of the public database MassIVE uncovers novel PFAS in samples from seven countries. Here, we reveal the historic and global presence of novel PFAS, providing guidance for the management and policy-making concerning persistent chemicals., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Rationale: The bulbs of Narcissus tazetta L. var. chinensis Roem (BNTCR) has been used as an herbal medicine for the treatment of carbuncles in China. The previous phytochemical studies indicated that the main active components of BNTCR were Amarllidaceae alkaloids (AAs). However, the comprehensive chemical and metabolic profiling of AAs in BNTCR remains underexplored. Therefore, it is necessary to establish an accurate and rapid approach for chemical characterization and metabolic profiling of AAs in BNTCR., Methods: The structural elucidation of natural products by ultra-high performance liquid chromatography coupled to quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS/MS) with outstanding properties is still limited due to complex mixtures that contain hundreds of compounds. In this study, we employed UHPLC-QTOF-MS/MS with a data mining strategy, including diagnostic fragment ions (DFIs) and neutral loss (NL), for rapid classifying and identifying AAs in BNTCR and prototypes absorbed into the blood of mice after oral administration of BNTCR extract., Results: As a result, 87 AAs, including 13 narciclasine type, 13 homolycorine type, 17 lycorine type, 12 galanthamine type, 25 haemanthamine type, and 7 tazettine type, were tentatively characterized (6 verified with authentic standards). In addition, a total of 44 prototypes were identified in mice plasma, urine, and fecal samples. Among them, 8 prototypes were found in plasma, 36 in urine, and 40 in feces, respectively. Lycorine, pseudolycorine, homolycorine, 8-O-demetylhomolycorine, and tazettine, as major AAs in BNTCR, were absorbed into the blood., Conclusions: Our findings provided a crucial step for the elucidation of the active compounds in BNTCR for treatment of carbuncles. In the future, the developed strategy could also be applied for identifying AAs in other herbal medicines from Amarllidaceae., (© 2024 John Wiley & Sons Ltd.)

Soybean is a globally important crop for food, feed, oil, and nitrogen fixation. A variety of multi-omics studies have been carried out, generating datasets ranging from genotype to phenotype. In order to efficiently utilize these data for basic and applied research, a soybean multi-omics database with extensive data coverage and comprehensive data analysis tools was established. The Soybean Omics Database (SoyOD) integrates important new datasets with existing public datasets to form the most comprehensive collection of soybean multi-omics information. Compared to existing soybean databases, SoyOD incorporates an extensive collection of novel data derived from the deep-sequencing of 984 germplasms, 162 novel transcriptomic datasets from seeds at different developmental stages, 53 phenotypic datasets, and more than 2500 phenotypic images. In addition, SoyOD integrates existing data resources, including 59 assembled genomes, genetic variation data from 3904 soybean accessions, 225 sets of phenotypic data, and 1097 transcriptomic sequences covering 507 different tissues and treatment conditions. Moreover, SoyOD can be used to mine candidate genes for important agronomic traits, as shown in a case study on plant height. Additionally, powerful analytical and easy-to-use toolkits enable users to easily access the available multi-omics datasets, and to rapidly search genotypic and phenotypic data in a particular germplasm. The novelty, comprehensiveness, and user-friendly features of SoyOD make it a valuable resource for soybean molecular breeding and biological research. SoyOD is publicly accessible at https://bis.zju.edu.cn/soyod., (© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.)

Background: In online mental health communities, the interactions among members can significantly reduce their psychological distress and enhance their mental well-being. The overall quality of support from others varies due to differences in people's capacities to help others. This results in some support seekers' needs being met, while others remain unresolved., Objective: This study aimed to examine which characteristics of the comments posted to provide support can make support seekers feel better (ie, result in cognitive change)., Methods: We used signaling theory to model the factors affecting cognitive change and used consulting strategies from the offline, face-to-face psychological counseling process to construct 6 characteristics: intimacy, emotional polarity, the use of first-person words, the use of future-tense words, specificity, and language style. Through text mining and natural language processing (NLP) technology, we identified linguistic features in online text and conducted an empirical analysis using 12,868 online mental health support reply data items from Zhihu to verify the effectiveness of those features., Results: The findings showed that support comments are more likely to alter support seekers' cognitive processes if those comments have lower intimacy (β intimacy =-1.706, P<.001), higher positive emotional polarity (β emotional&#95;polarity =.890, P<.001), lower specificity (β specificity =-.018, P<.001), more first-person words (β first-person =.120, P<.001), more future- and present-tense words (β future-words =.301, P<.001), and fewer function words (β linguistic&#95;style =-.838, P<.001). The result is consistent with psychotherapists' psychotherapeutic strategy in offline counseling scenarios., Conclusions: Our research contributes to both theory and practice by proposing a model to reveal the factors that make support seekers feel better. The findings have significance for support providers. Additionally, our study offers pointers for managing and designing online communities for mental health., (©Min Li, Dongxiao Gu, Rui Li, Yadi Gu, Hu Liu, Kaixiang Su, Xiaoyu Wang, Gongrang Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.01.2025.)

Egg quality is affected by lot of factors. Study was conducted to compare performance of data mining algorithms; Classification and regression tree (CART), Chi-square automatic interaction detection (CHAID), Exhaustive chi-square automatic interaction detection (Ex-CHAID) and Multivariate adaptive regression spline (MARS) in prediction of Potchefstroom Koekoek's eggshell thickness from egg quality traits. 350 eggs were collected at 31st to 39th week to examine the egg quality traits. MARS with R 2 (0.86) revealed yolk ratio, shell weight, egg shape index, yolk ratio, shell ratio, albumen weight and albumen ratio as explanatory variables predicting eggshell thickness. CART with R 2 (0.37), yolk/albumen ratio was noted to be influential predictor of eggshell thickness. CHAID and Ex-CHAID (R 2 = 0.35) discovered egg weight as the best predictor of eggshell thickness. MARS with R 2 (0.86) revealed yolk ratio, shell weight, egg shape index, yolk ratio, shell ratio, albumen weight and albumen ratio as explanatory variables predicting eggshell thickness. MARS had high r (0.925), R 2 (0.856) and lower RMSE (0.129) and AIC (-975.331) compared to CHAID, Ex-CHAID and CART leading MARS to be the best data mining algorithm when predicting the eggshell thickness using egg quality traits., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Statement of Animal rights: The study before commencement was approved by University of Limpopo Animal Research and Ethics Committee (ULAREC). The study followed ethical standards. Ethical approval: University of Limpopo Animal Research and Ethics Committee (ULAREC) approved the project with project number: AREC/55/2023: PG., (© 2025. The Author(s).)

Objective: Perfect policy system support is the key to effective work, and policy evaluation can provide theoretical basis for policy revision. This study aims to quantitatively evaluate the two-way referral policy in China and thus improve the policy system of two-way referral., Methods: This research focuses on 14 two-way referral policies published from 2007 to 2021. Basing on the focus of the two-way referral policy identified by text-mining methods, we constructed a PMC index model containing 9 primary and 43 secondary variables to conduct a quantitative evaluation of the two-way referral policy., Results: All document types are work notices. The two-way referral policy issued by relevant departments have gradually increased since 2015.The average PMC index of the 14 documents was 5.84, and the policy with the highest PMC index score was the policy P14 issued by Jiangsu province. There are 6 excellent policies, 8 good policies, and no qualified grade policy. The difference in PMC index scores mainly shows the three dimensions of policy clarity, policy content and policy coverage., Conclusions: This study indicates that the quality of two-way referral policy is generally good, which reveals the factors affecting the policy operability and contributes to expand the research perspective of two-way referral policy evaluation.We also analyzed the advantages and disadvantages of the existing policy of two-way referral through policy evaluation, and provided guidance for subsequent policy revisions., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mechanistic approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview of emerging integrated models applied across the spectrum of infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our review highlights the practical value of integrated models, including advances in disease forecasting, model parameterization, and calibration. However, key research gaps remain. These include the need for better incorporation of realistic decision-making considerations, expanded exploration of diverse datasets, and further investigation into biological and socio-behavioral mechanisms. Addressing these gaps will unlock the synergistic potential of AI and mechanistic modeling to enhance understanding of disease dynamics and support more effective public health planning and response., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Background: Natural language processing (NLP) enables the extraction of information embedded within unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying relevant medical concepts, NLP facilitates the generation of structured and actionable data, supporting complex tasks like cohort identification and the analysis of clinical records. To accomplish those tasks, we introduce a deep learning-based and lexicon-based named entity recognition (NER) tool for texts in Spanish. It performs medical NER and normalization, medication information extraction and detection of temporal entities, negation and speculation, and temporality or experiencer attributes (Age, Contraindicated, Negated, Speculated, Hypothetical, Future, Family&#95;member, Patient and Other). We built the tool with a dedicated lexicon and rules adapted from NegEx and HeidelTime. Using these resources, we annotated a corpus of 1200 texts, with high inter-annotator agreement (average F1 = 0.841% ± 0.045 for entities, and average F1 = 0.881% ± 0.032 for attributes). We used this corpus to train Transformer-based models (RoBERTa-based models, mBERT and mDeBERTa). We integrated them with the dictionary-based system in a hybrid tool, and distribute the models via the Hugging Face hub. For an internal validation, we used a held-out test set and conducted an error analysis. For an external validation, eight medical professionals evaluated the system by revising the annotation of 200 new texts not used in development., Results: In the internal validation, the models yielded F1 values up to 0.915. In the external validation with 100 clinical trials, the tool achieved an average F1 score of 0.858 (± 0.032); and in 100 anonymized clinical cases, it achieved an average F1 score of 0.910 (± 0.019)., Conclusions: The tool is available at https://claramed.csic.es/medspaner . We also release the code ( https://github.com/lcampillos/medspaner ) and the annotated corpus to train the models., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare that they have no Conflict of interest., (© 2025. The Author(s).)

Background: Sentiment analysis is one of the most widely used methods for mining and examining text. Social media researchers need guidance on choosing between manual and automated sentiment analysis methods., Objective: Popular sentiment analysis tools based on natural language processing (NLP; VADER [Valence Aware Dictionary for Sentiment Reasoning], TEXT2DATA [T2D], and Linguistic Inquiry and Word Count [LIWC-22]), and a large language model (ChatGPT 4.0) were compared with manually coded sentiment scores, as applied to the analysis of YouTube comments on videos discussing the opioid epidemic. Sentiment analysis methods were also examined regarding ease of programming, monetary cost, and other practical considerations., Methods: Evaluation methods included descriptive statistics, receiver operating characteristic (ROC) curve analysis, confusion matrices, Cohen κ, accuracy, specificity, precision, sensitivity (recall), F 1 -score harmonic mean, and the Matthews correlation coefficient. An inductive, iterative approach to content analysis of the data was used to obtain manual sentiment codes., Results: A subset of comments were analyzed by a second coder, producing good agreement between the 2 coders' judgments (κ=0.734). YouTube social media about the opioid crisis had many more negative comments (4286/4871, 88%) than positive comments (79/662, 12%), making it possible to evaluate the performance of sentiment analysis models in an unbalanced dataset. The tone summary measure from LIWC-22 performed better than other tools for estimating the prevalence of negative versus positive sentiment. According to the ROC curve analysis, VADER was best at classifying manually coded negative comments. A comparison of Cohen κ values indicated that NLP tools (VADER, followed by LIWC's tone and T2D) showed only fair agreement with manual coding. In contrast, ChatGPT 4.0 had poor agreement and failed to generate binary sentiment scores in 2 out of 3 attempts. Variations in accuracy, specificity, precision, sensitivity, F 1 -score, and MCC did not reveal a single superior model. F 1 -score harmonic means were 0.34-0.38 (SD 0.02) for NLP tools and very low (0.13) for ChatGPT 4.0. None of the MCCs reached a strong correlation level., Conclusions: Researchers studying negative emotions, public worries, or dissatisfaction with social media face unique challenges in selecting models suitable for unbalanced datasets. We recommend VADER, the only cost-free tool we evaluated, due to its excellent discrimination, which can be further improved when the comments are at least 100 characters long. If estimating the prevalence of negative comments in an unbalanced dataset is important, we recommend the tone summary measure from LIWC-22. Researchers using T2D must know that it may only score some data and, compared with other methods, be more time-consuming and cost-prohibitive. A general-purpose large language model, ChatGPT 4.0, has yet to surpass the performance of NLP models, at least for unbalanced datasets with highly prevalent (7:1) negative comments., (©Lisa M Gandy, Lana V Ivanitskaya, Leeza L Bacon, Rodina Bizri-Baryak. Originally published in JMIR Formative Research (https://formative.jmir.org), 08.01.2025.)

Many practical disaster reports are published daily worldwide in various forms, including after-action reports, response plans, impact assessments, and resiliency plans. These reports serve as vital resources, allowing future generations to learn from past events and better mitigate and prepare for future disasters. However, this extensive practical literature often has limited impact on research and practice due to challenges in synthesizing and analyzing the reports. In this study, we 1) present a corpus of practical reports for text mining and 2) introduce an approach to extract insights from the corpus using select text mining tools. We validate the approach through a case study examining practical reports on the preparedness of the U.S. Pacific Northwest for a magnitude 9 Cascadia Subduction Zone earthquake, which has the potential to disrupt lifeline infrastructures for months. To explore opportunities and challenges associated with text mining of practical disaster reports, we conducted a brief survey of potential user groups. The case study illustrates the types of insights that our approach can extract from a corpus. Notably, it reveals potential differences in priorities between Washington and Oregon state-level emergency management, uncovers latent sentiments expressed within the reports, and identifies inconsistent vocabulary across the field. Survey results highlight that while simple tools may yield insights that are primarily interpretable by experienced professionals, more advanced tools utilizing large language models, such as Generative Pre-trained Transformer (GPT), offer more accessible insights, albeit with known risk associated with current artificial intelligence technologies. To ensure reproducibility, all supporting data and code are made publicly available (DOI: 10.17603/ds2-9s7w-9694)., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Background: Social media hashtags play a significant role in increasing the visibility of health information by making it easier for people to explore health-related content. Health promotion campaigns use campaign-specific hashtags to disseminate health-related messages, enabling individuals to access accurate and timely resources and updates. The study aims to discover patterns of connection between hashtags and identify the most influential hashtags used on Twitter in the American Heart Month campaigns., Method: We collected a total of 73,288 tweets containing #AmericanHeartMonth between January 2019 and March 2023 and retrieved 18,143 original tweets, 42,930 retweets, 2,519 quotes, and 20,846 likes related to the past five campaigns. We adapted co-occurrence network analysis to explore the patterns of relationships between hashtags and association rules mining to assess the quality and strength of association between the co-occurred hashtags., Result: While #AmericanHeartMonth, #OurHearts, and #HeartMonth play central roles in all hashtag co-occurrence networks, the results of association rules mining indicate a significant association of #OurHearts within the networks. The highest density of hashtags has been observed in the quoted tweets, introducing a new range of hashtags such as #GoRedForWomen, #WearRedDay, #HeartDisease, and #HeartHealth by Twitter users, indicating the positive correlation between co-occurring hashtags and users' engagement. The results of quality measurements of association rules (Lift > 1) indicate positive relationships between the co-occurred hashtags in the top 5 rules in all data subsets., Conclusion: We employed co-occurrence network analysis and association rules mining as powerful techniques to identify influential hashtags that may have a central role in health-related discussions and drive engagement within the co-occurrence hashtag network. In conclusion, we recommend additional hashtag structures in conjunction with heart health-related topics to improve community building and the effectiveness of disseminating messages in future heart health promotion campaigns. The study contributes to knowledge and practice by offering a structured and data-driven approach and providing practical guidance for public health practitioners, professionals, and organisations to optimise content, targeting, and messaging to reach and engage a broader audience with health-related information., Competing Interests: Declarations. Consent for publication: The data used in this study were collected from publicly accessible posts on Twitter in compliance with Twitter's terms of use. No identifiable personal data were collected or used, and all data have been anonymised to ensure privacy. As the data are publicly available, explicit consent to publish is not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

iModulons-sets of co-expressed genes identified through independent component analysis (ICA) of high-quality transcriptomic datasets-provide an unbiased, modular view of an organism's transcriptional regulatory network. Established in 2020, iModulonDB (iModulonDB.org) serves as a centralized repository of curated iModulon sets, enabling users to explore iModulons and download the associated transcriptomic data. This update reflects a significant expansion of the database-19 new ICA decompositions (+633%) spanning 8 925 expression profiles (+1370%), 503 studies (+2290%) and 12 additional organisms (+400%)-and introduces new features to help scientists decipher the mechanisms governing prokaryotic transcriptional regulation. To facilitate comprehension of the underlying expression profiles, the updated user-interface displays essential information about each data-generating study (e.g. the experimental conditions and publication abstract). Dashboards now include condition-specific coloring and highlight data generated from genetically perturbed strains, enabling users to rapidly interpret disruptions in transcriptional regulation. New interactive graphs rapidly convey omics-derived indicators (e.g. the explained variance of ICA decompositions, genetic overlap between iModulons and regulons). Direct links to operon diagrams (BioCyc) and protein-protein interaction networks (STRING) provide users with seamless access to external resources for further assessment of iModulons. Lastly, a new suite of search-driven and species-wide analysis tools promotes user-engagement with iModulons, reinforcing iModulonDB's role as a dynamic, interactive knowledgebase of prokaryotic transcriptional regulation., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The design, analysis and mining of large-scale 'omics studies with the goal of advancing biological and biomedical understanding require use of a range of bioinformatics tools, including approaches tailored to needs specific to a given species and/or technology. The FlyRNAi database at the Drosophila RNAi Screening Center and Transgenic RNAi Project (DRSC/TRiP) Functional Genomics Resources (https://fgr.hms.harvard.edu/tools) supports an increasingly broad group of technologies and species. Recently, for example, we expanded the database to include additional new data-centric resources that facilitate mining and analysis of single-cell transcriptomics. In addition, we have applied our approaches to CRISPR reagent and gene-centric bioinformatics approaches in Drosophila to arthropod vectors of infectious diseases. Building on our previous comprehensive reports on the FlyRNAi database, here we focus on new and updated resources with a primary focus on data-centric tools. Altogether, our suite of online resources supports various stages of functional genomics studies for Drosophila and other arthropods, and facilitate a wide range of reagent design, analysis, data mining and analysis approaches by biologists and biomedical experts studying Drosophila, other common genetic model species, arthropod vectors and/or human biology., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Community-acquired pneumonia (CAP) is associated with high mortality rates and often results in prolonged hospital stays. The potential of machine learning to enhance prediction accuracy in this context is significant, yet clinicians often lack the programming skills required for effective data mining. This study aimed to assess the effectiveness of a low-code approach for assisting clinicians with data mining for mortality and length of stay (LOS) prediction in patients with CAP. A retrospective study was conducted using a low-code platform and the PyCaret library in Google Colab on data from patients with community-acquired pneumonia (CAP) admitted between January 2013 and December 2021 to two medical facilities. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) for mortality prediction and the R2 score for LOS prediction, with benchmarks set at AUC > 0.9 and R2 > 0.5. The Shapley Additive Explanations (SHAP) method was used for interpreting individual predictions. A total of 669 CAP patients were enrolled in the analysis.Fifteen models were evaluated for mortality prediction, and nineteen models were evaluated for LOS prediction utilizing the PyCaret library. The Light Gradient Boosting Machine model yielded the highest AUC (0.963) for mortality prediction. In predicting LOS, the Extratrees Regressor model achieved the highest R2 score of 0.585. Factors such as the severity of pneumonia and the Charlson Comorbidity Index (CCI) were significant factors influencing mortality. For the LOS, the CCI score, activities of daily living, and social support were significant predictors. The low-code approach enables medical professionals with limited technical expertise to effectively employ data science in their clinical decision-making process. This approach proved to be a valuable tool in the analysis of CAP patient data., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Promoters are essential DNA sequences that initiate transcription and regulate gene expression. Precisely identifying promoter sites is crucial for deciphering gene expression patterns and the roles of gene regulatory networks. Recent advancements in bioinformatics have leveraged deep learning and natural language processing (NLP) to enhance promoter prediction accuracy. Techniques such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and BERT models have been particularly impactful. However, current approaches often rely on arbitrary DNA sequence segmentation during BERT pre-training, which may not yield optimal results. To overcome this limitation, this article introduces a novel DNA sequence segmentation method. This approach develops a more refined dictionary for DNA sequences, utilizes it for BERT pre-training, and employs an Inception neural network as the foundational model. This BERT-Inception architecture captures information across multiple granularities. Experimental results show that the model improves the performance of several downstream tasks and introduces deep learning interpretability, providing new perspectives for interpreting and understanding DNA sequence information. The detailed source code is available at https://github.com/katouMegumiH/Promoter&#95;BERT ., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: In this study, we evaluate the accuracy, efficiency, and cost-effectiveness of large language models in extracting and structuring information from free-text clinical reports, particularly in identifying and classifying patient comorbidities within oncology electronic health records. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators., Objective: We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators., Methods: We implemented a script using the OpenAI application programming interface to extract structured information in JavaScript object notation format from comorbidities reported in 250 personal history reports. These reports were manually reviewed in batches of 50 by 5 specialists in radiation oncology. We compared the results using metrics such as sensitivity, specificity, precision, accuracy, F-value, κ index, and the McNemar test, in addition to examining the common causes of errors in both humans and generative pretrained transformer (GPT) models., Results: The GPT-3.5 model exhibited slightly lower performance compared to physicians across all metrics, though the differences were not statistically significant (McNemar test, P=.79). GPT-4 demonstrated clear superiority in several key metrics (McNemar test, P<.001). Notably, it achieved a sensitivity of 96.8%, compared to 88.2% for GPT-3.5 and 88.8% for physicians. However, physicians marginally outperformed GPT-4 in precision (97.7% vs 96.8%). GPT-4 showed greater consistency, replicating the exact same results in 76% of the reports across 10 repeated analyses, compared to 59% for GPT-3.5, indicating more stable and reliable performance. Physicians were more likely to miss explicit comorbidities, while the GPT models more frequently inferred nonexplicit comorbidities, sometimes correctly, though this also resulted in more false positives., Conclusions: This study demonstrates that, with well-designed prompts, the large language models examined can match or even surpass medical specialists in extracting information from complex clinical reports. Their superior efficiency in time and costs, along with easy integration with databases, makes them a valuable tool for large-scale data mining and real-world evidence generation., (©Amadeo Jesus Wals Zurita, Hector Miras del Rio, Nerea Ugarte Ruiz de Aguirre, Cristina Nebrera Navarro, Maria Rubio Jimenez, David Muñoz Carmona, Carlos Miguez Sanchez. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 02.01.2025.)

Molecular structure prediction and homology detection offer promising paths to discovering protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a statistically principled approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model. Our method (1) lets users select many biologically-relevant loss metrics (i.e. false discovery rate) and assigns reliable functional probabilities for annotating genes of unknown function; (2) achieves state-of-the-art performance in enzyme classification without training new models; and (3) robustly and rapidly pre-filters proteins for computationally intensive structural alignment algorithms. Our framework enhances the reliability of protein homology detection and enables the discovery of uncharacterized proteins with likely desirable functional properties., Competing Interests: Competing interests: J.A.D. is a co-founder of Caribou Biosciences, Editas Medicine, Intellia Therapeutics, Mammoth Biosciences and Scribe Therapeutics, and a director of Altos, Johnson & Johnson and Tempus. J.A.D. is a scientific advisor to Caribou Biosciences, Intellia Therapeutics, Mammoth Biosciences, Inari, Scribe Therapeutics, Felix Biosciences and Algen. J.A.D. also serves as Chief Science Advisor to Sixth Street and a Scientific Advisory Board member at The Column Group. J.A.D. conducts academic research projects sponsored by Roche and Apple Tree Partners. The remaining authors declare no competing interests., (© 2025. The Author(s).)

Objectives: Metabolic syndrome (MetS) has a significant impact on health. MetS is the umbrella term for a group of interdependent metabolic threats that contribute to the emergence of diseases that can lead to death. This study was designed to better predict the risks associated with MetS to enable medical personnel to make more optimal preventive medical decisions. Study design: Data from a large hospital survey database was used to train data mining classification techniques to predict patient-level risk subsequent to extensive data engineering that included aggregating predictors from multiple visits. Methods: A prospective group of seemingly healthy volunteers from the database was studied based on data obtained during their regular annual health checkups. Results: After aggregating the variables over time, the findings indicated that the predictive power of our model outperformed methods presented in other studies (AUC = 0.947). Specific lifestyle factors were identified as contributing to MetS. Conclusion: Involvement to avoid recurring diseases can significantly decrease medical problems and treatment expenses. The findings emphasize the importance of using predictive tools in healthcare and preventive medicine. The results can be used for future prevention strategies that encourage lifestyle changes and implement directed medical treatment protocols to decrease the burden of illness., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The recent severe acute respiratory syndrome coronavirus 2 pandemic has clearly exemplified the need for broad-spectrum antiviral (BSA) medications. However, previous outbreaks show that about one year after an outbreak, interest in antiviral research diminishes and the work toward an effective medication is left unfinished. Martin et al. endeavored to support the early stages of focused BSA development by creating the Small Molecule Antiviral Compound Collection (SMACC), which is publicly available online at https://smacc.mml.unc.edu. SMACC is a highly curated database with over 32,500 entries of chemical compounds tested in both phenotypic and target-based assays across 13 viruses from the NIAID's list of emerging infectious diseases/pathogens. The authors advise judicious use of knowledge collections such as SMACC and recommend users critically evaluate retrieved data and resulting hypotheses prior to experimental testing. When used correctly, SMACC-like databases may serve as a reference for medicinal chemists and virologists working to develop novel BSA medications. To summarize, we emphasize the importance of data curation for both novel outbreak prediction and development of BSAs against these outbreaks., Competing Interests: Declaration of conflicting interestsThe authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: AT and ENM are co-founders of Predictive, LLC, which develops novel alternative methods and software for toxicity prediction. All other authors have nothing to disclose.

Background: Available codes in the ICD-10 do not accurately reflect soft tissue sarcoma diagnoses, and this can result in an underrepresentation of soft tissue sarcoma in databases. The National VA Database provides a unique opportunity for soft tissue sarcoma investigation because of the availability of all clinical results and pathology reports. In the setting of soft tissue sarcoma, natural language processing (NLP) has the potential to be applied to clinical documents such as pathology reports to identify soft tissue sarcoma independent of ICD codes, allowing sarcoma researchers to build more comprehensive databases capable of answering a myriad of research questions., Questions/purposes: (1) What proportion of patients with myxofibrosarcoma within the National VA Database would be missed by searching only by soft tissue sarcoma ICD codes? (2) Is a de novo NLP algorithm capable of analyzing pathology reports to accurately identify patients with myxofibrosarcoma?, Methods: All pathology reports (10.7 million) in the national VA corporate data warehouse were identified from 2003 to 2022. Using the word-search functionality, reports from 403 veterans were found to contain the term "myxofibrosarcoma." The resulting pathology reports were manually reviewed to develop a gold-standard cohort that contained only those veterans with pathologist-confirmed myxofibrosarcoma diagnoses. The cohort had a mean ± SD age of 70 ± 12 years, and 96% (287 of 300) were men. Diagnosis codes were abstracted, and differences in appropriate ICD coding were compared. An NLP algorithm was iteratively refined and tested using confounders, negation, and emphasis terms for myxofibrosarcoma. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for the NLP-generated cohorts through comparison with the manually reviewed gold-standard cohorts., Results: The records of 27% (81 of 300) of myxofibrosarcoma patients within the VA database were missing a sarcoma ICD code. A de novo NLP algorithm more accurately (92% [276 of 300]) identified patients with myxofibrosarcoma compared with ICD codes (73% [219 of 300]) or basic word searches (74% [300 of 403]) (p < 0.001). Three final algorithm models were generated with accuracies ranging from 92% to 100%., Conclusion: An NLP algorithm can identify patients with myxofibrosarcoma from pathology reports with high accuracy, which is an improvement over ICD-based cohort creation and simple word search. This algorithm is freely available on GitHub ( https://github.com/sarcoma-shark/myxofibrosarcoma-shark ) and is available to facilitate external validation and improvement through testing in other cohorts., Level of Evidence: Level II, diagnostic study., Competing Interests: Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2024 by the Association of Bone and Joint Surgeons.)

Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required to obtain labels for data in the real world. Most of the existing embedded UFS utilize a sparse projection matrix for FS. However, this may introduce additional regularization terms, and it is difficult to control the sparsity of the projection matrix well. Moreover, such methods may seriously destroy the original feature structure in the embedding space. Instead, avoiding projecting the original data into the low-dimensional embedding space and identifying features directly from the raw features that perform well in the process of making the data show a distinct cluster structure is a feasible solution. Inspired by this, this paper proposes a model called A General Adaptive Unsupervised Feature Selection with Auto-weighting (GAWFS), which utilizes two techniques, non-negative matrix factorization, and adaptive graph learning, to simulate the process of dividing data into clusters, and identifies the features that are most discriminative in the clustering process by a feature weighting matrix Θ. Since the weighting matrix is sparse, it also plays the role of FS or a filter. Finally, experiments comparing GAWFS with several state-of-the-art UFS methods on synthetic datasets and real-world datasets are conducted, and the results demonstrate the superiority of the GAWFS., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using post-mortem samples of brain tissue or carefully recruited clinical trial patients. While these resources are excellent, they suffer from lack of sex/gender, and racial/ethnic inclusiveness. Electronic Health Records (EHR) data has the potential to bridge this gap by including real-world ADRD patients treated during routine clinical care. In this study, we utilize EHR data from a cohort of 70,420 ADRD patients diagnosed and treated at Penn Medicine. Our goal is to uncover important risk features leading to three types of Neuro-Degenerative Disorders (NDD), including Alzheimer's Disease (AD), Parkinson's Disease (PD) and Other Dementias (OD). We employ a variety of Machine Learning (ML) Methods, including uni-variate and multivariate ML approaches and compare accuracies across the ML methods. We also investigate the types of features identified by each method, the overlapping features and the unique features to highlight important advantages and disadvantages of each approach specific for certain NDD types. Our study is important for those interested in studying ADRD and NDD in EHRs as it highlights the strengths and limitations of popular approaches employed in the ML community. We found that the uni-variate approach was able to uncover features that were important and rare for specific types of NDD (AD, PD, OD), which is important from a clinical perspective. Features that were found across all methods represent features that are the most robust.

Multi-view clustering has become a rapidly growing field in machine learning and data mining areas by combining useful information from different views for last decades. Although there have been some surveys based on multi-view clustering, most of these works ignore simultaneously taking the self-supervised and non-self supervised multi-view clustering into consideration. We give a novel survey for sorting out the existing algorithms of multi-view clustering in this work, which can be classified into two different categories, i.e., non-self supervised and self-supervised multi-view clustering. We first review the representative approaches based on the non-self supervised multi-view clustering, which consist of methods based on non-representation learning and representation learning. Furthermore, the methods built on non-representation learning contain works based on matrix factorization, kernel and other non-representation learning. Methods based on representation learning consist of multi-view graph clustering, deep representation learning and multi-view subspace clustering. For the methods based on self-supervised multi-view clustering, we divide them into contrastive methods and generative methods. Overall, this survey attempts to give an insightful overview regarding the developments in the multi-view clustering field., Competing Interests: Declaration of competing interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT) excel at interpreting unstructured data from public sources, yet are limited when responding to queries on private repositories, such as electronic health records (EHRs). We hypothesized that prompt engineering could enhance the accuracy of LLMs for interpreting EHR data without requiring domain knowledge, thus expanding their utility for patients and personalized diagnostics., Methods: We designed and systematically tested prompt engineering techniques to improve the ability of LLMs to interpret EHRs for nuanced diagnostic questions, referenced to a panel of medical experts. In 490 full-text EHR notes from 125 patients with prior life-threatening heart rhythm disorders, we asked GPT-4-turbo to identify recurrent arrhythmias distinct from prior events and tested 220 563 queries. To provide context, results were compared with rule-based natural language processing and Bidirectional Encoder Representations from Transformer-based language models. Experiments were repeated for 2 additional LLMs., Results: In an independent hold-out set of 389 notes, GPT-4-turbo had a balanced accuracy of 64.3%±4.7% out-of-the-box at baseline. This increased when asking GPT-4-turbo to provide a rationale for its answers, a structured data output, and in-context exemplars, to a balanced accuracy of 91.4%±3.8% ( P <0.05). This surpassed the traditional logic-based natural language processing and BERT-based models ( P <0.05). Results were consistent for GPT-3.5-turbo and Jurassic-2 LLMs., Conclusions: The use of prompt engineering strategies enables LLMs to identify clinical end points from EHRs with an accuracy that surpassed natural language processing and approximated experts, yet without the need for expert knowledge. These approaches could be applied to LLM queries for other domains, to facilitate automated analysis of nuanced data sets with high accuracy by nonexperts., Competing Interests: Dr Narayan reports consulting for Abbott, Inc, UpToDate, Inc, and TDK, Inc; equity interest in Life Signals.ai and PhysCade, Inc; and intellectual property owned by the University of California Regents and Stanford University. Dr Rogers reports consulting for TDK, Inc. Dr Rodrigo holds equity interests in Corify Health Care. Dr Baykaner reports consulting honoraria to the institution from Abbott, Boston Scientific, and Daiichi Sankyo; no personal gain. P. Clopton reports consulting for the American College of Cardiology. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The other authors report no conflicts.

Spatiotemporal Graph (STG) forecasting is an essential task within the realm of spatiotemporal data mining and urban computing. Over the past few years, Spatiotemporal Graph Neural Networks (STGNNs) have gained significant attention as promising solutions for STG forecasting. However, existing methods often overlook two issues: the dynamic spatial dependencies of urban networks and the heterogeneity of urban spatiotemporal data. In this paper, we propose a novel framework for STG learning called Dynamic Meta-Graph Convolutional Recurrent Network (DMetaGCRN), which effectively tackles both challenges. Specifically, we first build a meta-graph generator to dynamically generate graph structures, which integrates various dynamic features, including input sensor signals and their historical trends, periodic information (timestamp embeddings), and meta-node embeddings. Among them, a memory network is used to guide the learning of meta-node embeddings. The meta-graph generation process enables the model to simulate the dynamic spatial dependencies of urban networks and capture data heterogeneity. Then, we design a Dynamic Meta-Graph Convolutional Recurrent Unit (DMetaGCRU) to simultaneously model spatial and temporal dependencies. Finally, we formulate the proposed DMetaGCRN in an encoder-decoder architecture built upon DMetaGCRU and meta-graph generator components. Extensive experiments on four real-world urban spatiotemporal datasets validate that the proposed DMetaGCRN framework outperforms state-of-the-art approaches., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: This study aims to address the gap in the literature on converting real-world Clinical Document Architecture (CDA) data into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), focusing on the initial steps preceding the mapping phase. We highlight the importance of a repeatable Extract-Transform-Load (ETL) pipeline for health data extraction from HL7 CDA documents in Estonia for research purposes., Methods: We developed a repeatable ETL pipeline to facilitate the extraction, cleaning, and restructuring of health data from CDA documents to OMOP CDM, ensuring a high-quality and structured data format. This pipeline was designed to adapt to continuously updated data exchange format changes and handle various CDA document subsets for different scientific studies., Results: We demonstrated via selected use cases that our pipeline successfully transformed a significant portion of diagnosis codes, body weight and eGFR measurements, and PAP test results from CDA documents into OMOP CDM, showing the ease of extracting structured data. However, challenges such as harmonising diverse coding systems and extracting lab results from free-text sections were encountered. The iterative development of the pipeline facilitated swift error detection and correction, enhancing the process's efficiency., Conclusion: After a decade of focused work, our research has led to the development of an ETL pipeline that effectively transforms HL7 CDA documents into OMOP CDM in Estonia, addressing key data extraction and transformation challenges. The pipeline's repeatability and adaptability to various data subsets make it a valuable resource for researchers dealing with health data. While tested on Estonian data, the principles outlined are broadly applicable, potentially aiding in handling health data standards that vary by country. Despite newer health data standards emerging, the relevance of CDA for retrospective health studies ensures the continuing importance of this work., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of such information., Materials and Methods: Psychiatric encounter notes from Mount Sinai Health System (MSHS, n = 300) and Weill Cornell Medicine (WCM, n = 225) were annotated to create a gold-standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (eg, social network, instrumental support, and loneliness)., Results: For extracting SS/SI, the RBS obtained higher macroaveraged F1-scores than the LLM at both MSHS (0.89 versus 0.65) and WCM (0.85 versus 0.82). For extracting the subcategories, the RBS also outperformed the LLM at both MSHS (0.90 versus 0.62) and WCM (0.82 versus 0.81)., Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across all metrics. An intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS was designed and refined to follow the same specific rules as the gold-standard annotations. Conversely, the LLM was more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages, although additional replication studies are warranted., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: In recent years, the incidence of rectal prolapse has increased significantly due to the sedentary lifestyle and irregular eating habits of modern life. However, there is a lack of clinical studies on the treatment of rectal prolapse with traditional Chinese medicine (TCM) with a large sample size. Therefore, this study investigated the characteristics of rectal prolapse treatment formulas and then studied the network pharmacology of their core therapeutic drugs, which can help to provide a reference for the treatment and postoperative care of rectal prolapse patients., Objective: This study aimed to explore the prescription characteristics and the mechanism of action of core drugs in the treatment of rectal prolapse in Chinese medicine through data mining and bioinformatics techniques., Methods: We collected the diagnosis and treatment information of patients with rectal prolapse from January 2014 to September 2021 in the electronic case database of Nanjing Hospital of TCM, mined the patient information and prescription features using R, screened the active ingredients of the core pairs of drugs and disease drug intersection targets using TCMSP and GnenCard databases, and constructed a Protein-protein interaction (PPI) network using STRING and Cytoscape, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the intersecting targets were performed using Metascape and R., Results: We found that prolapse is easy to occur in people over 50 years old, preferably in autumn and winter. Commonly used therapeutic Chinese medicines include Glycyrrhiza glabra, Radix angelicae sinensis, Radix astragali, Atractylodes macrocephala, and Pericarpium citri reticulatae , which are mostly deficiency tonic medicines, warm in nature, and belong to spleen meridian. The core therapeutic medicinal pair was "Bupleuri radix-Cimicifugae rhizoma". There were 190 common targets of Bupleuri radix and Cimicifugae rhizoma , and 71 intersection targets of the drug pair and prolapse. The main components of the core drugs for the treatment of prolapse may be quercetin, kaempferol, Stigmasterol, etc, and the core targets may be CASP3, AKT1, HIF1A, etc. The total number of GO entries for the intersection targets of "Bupleuri radix-Cimicifugae rhizoma" and diseases was 3495, among which the molecular functions accounted for the largest proportion, mainly Pathways in cancer, IL-18 signaling pathway, etc . KEGG enriched pathway analysis yielded 168 results, and the major pathways were pathways in cancer, lipid and atherosclerosis, IL-17 signaling pathway, etc. Conclusion: This study adopted real-world research methodology and used data mining and bioinformatics technology to mine the medication law of rectal prolapse and its core drug action mechanism from the clinical information of Chinese medicine., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Technological innovation serves as the catalyst for the shift towards circular practices. Technologies not only address technical challenges, facilitating the transition to a more circular economy, but they also enhance business efficiency and profitability. Furthermore, they promote inclusivity and create job opportunities, ultimately yielding positive societal impacts. The research in this area tends to focus on digital technologies, neglecting other technological areas. Moreover, it heavily relies on literature reviews and expert opinions, potentially introducing biases. In this article we investigate the technological landscape of the circular economy through Natural Language Processing (NLP), examining key technologies used in this sector and the primary challenges in managing these technologies. The methodology is applied to more than 45,000 scientific publications and aims to extract technologies in the text of scientific articles with NLP. The findings of our analysis reveal a strong emphasis on emerging digital, life cycle assessment and biomaterials technologies. Furthermore, we identified seven distinct technological domains within the CE field. Finally, we provide advantages and problems arising in the adoption and implementation of these technologies in an industrial context., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Giordano et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The aim of this study is to develop a method we call "cost mining" to unravel cost variation and identify cost drivers by modelling integrated patient pathways from primary care to the palliative care setting. This approach fills an urgent need to quantify financial strains on healthcare systems, particularly for colorectal cancer, which is the most expensive cancer in Australia, and the second most expensive cancer globally., Methods: We developed and published a customized algorithm that dynamically estimates and visualizes the mean, minimum, and total costs of care at the patient level, by aggregating activity-based healthcare system costs (e.g. DRGs) across integrated pathways. This extends traditional process mining approaches by making the resulting process maps actionable and informative and by displaying cost estimates. We demonstrate the method by constructing a unique dataset of colorectal cancer pathways in Victoria, Australia, using records of primary care, diagnosis, hospital admission and chemotherapy, medication, health system costs, and life events to create integrated colorectal cancer patient pathways from 2012 to 2020., Results: Cost mining with the algorithm enabled exploration of costly integrated pathways, i.e. drilling down in high-cost pathways to discover cost drivers, for 4246 cases covering approx. 4 million care activities. Per-patient CRC pathway costs ranged from $10,379 AUD to $41,643 AUD, and varied significantly per cancer stage such that e.g. chemotherapy costs in one cancer stage are different to the same chemotherapy regimen in a different stage. Admitted episodes were most costly, representing 93.34% or $56.6 M AUD of the total healthcare system costs covered in the sample., Conclusions: Cost mining can supplement other health economic methods by providing contextual, sequence and timing-related information depicting how patients flow through complex care pathways. This approach can also facilitate health economic studies informing decision-makers on where to target care improvement or to evaluate the consequences of new treatments or care delivery interventions. Through this study we provide an approach for hospitals and policymakers to leverage their health data infrastructure and to enable real time patient level cost mining., Competing Interests: Declarations. Ethics approval and consent to participate: The study received ethical approval by the Royal Melbourne Hospital Ethics Board through BioGrid application number 202003/8 (Australia). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)