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Objectives: This study aimed to comprehensively investigate the neuroanatomical alterations associated with idiopathic Ménière's disease (MD) using voxel-based morphometry and surface-based morphometry techniques. The primary objective was to explore nuanced changes in gray matter volume, cortical thickness, fractal dimension, gyrification index, and sulcal depth in MD patients compared with healthy controls (HC). Additionally, we sought to develop a machine learning classification model utilizing these neuroimaging features to effectively discriminate between MD patients and HC., Design: A total of 55 patients diagnosed with unilateral MD and 70 HC were enrolled in this study. Voxel-based morphometry and surface-based morphometry were employed to analyze neuroimaging data and identify structural differences between the two groups. The selected neuroimaging features were used to build a machine learning classification model for distinguishing MD patients from HC., Results: Our analysis revealed significant reductions in gray matter volume in MD patients, particularly in frontal and cingulate gyri. Distinctive patterns of alterations in cortical thickness were observed in brain regions associated with emotional processing and sensory integration. Notably, the machine learning classification model achieved an impressive accuracy of 84% in distinguishing MD patients from HC. The model's precision and recall for MD and HC demonstrated robust performance, resulting in balanced F1-scores. Receiver operating characteristic curve analysis further confirmed the discriminative power of the model, supported by an area under the curve value of 0.92., Conclusions: This comprehensive investigation sheds light on the intricate neuroanatomical alterations in MD. The observed gray matter volume reductions and distinct cortical thickness patterns emphasize the disease's impact on neural structure. The high accuracy of our machine learning classification model underscores its diagnostic potential, providing a promising avenue for identifying MD patients. These findings contribute to our understanding of MD's neural underpinnings and offer insights for further research exploring the functional implications of structural changes., Competing Interests: This work was supported by the National Natural Science Foundation of China (Grant No. 82101231, 81701673, 81670930), and the Hubei Provincial Natural Science Foundation of China (Grant No. 2021CFB547, 2019CFB497). There are no conflicts of interest, financial, or otherwise., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version. With its 27 items requiring professional administration, the original CDI proves to be a time-consuming method for predicting children and adolescents with high depression risk. Hence, this study aimed to develop a shortened version of the CDI to predict high depression risk, thereby enhancing the efficiency of prediction and intervention., Methods: Initially, backward elimination is conducted to identify various version of the short-form scales (e.g., three-item and five-item versions). Subsequently, the performance of five machine learning (ML) algorithms on these versions is evaluated using the area under the ROC curve (AUC) to determine the best algorithm. The chosen algorithm is then utilized to model the short-form scales, facilitating the identification of the optimal short-form scale based on predefined evaluation metrics. Following this, evaluation metrics are computed for all potential decision thresholds of the optimal short-form scale, and the threshold value is determined. Finally, the reliability and validity of the optimal short-form scale are assessed using a new sample., Results: The study identified a five-item short-form CDI with a decision threshold of 4 as the most appropriate scale considering all assessment indicators. The scale had 81.48% fewer items than the original version, indicating good predictive performance (AUC = 0.81, Accuracy = 0.83, Recall = 0.76, Precision = 0.71). Based on the test of 315 middle school students, the results showed that the five-item CDI had good measurement indexes (Cronbach's alpha = 0.72, criterion-related validity = 0.77)., Conclusions: This five-item short-form CDI is the first shortened and revised version of the CDI in China based on large local data samples., (© 2024. The Author(s).)

Efficient and reliable diagnosis of craniofacial patterns is critical to orthodontic treatment. Although machine learning (ML) is time-saving and high-precision, prior knowledge should validate its reliability. This study proposed a craniofacial ML diagnostic workflow base on a cephalometric geometric model through clinical verification. A cephalometric geometric model was established to determine the landmark location by analyzing 408 X-ray lateral cephalograms. Through geometric information and feature engineering, nine supervised ML algorithms were conducted for sagittal and vertical skeleton patterns. After dimension reduction, plane decision boundary and landmark contribution contours were depicted to demonstrate the diagnostic consistency and the consistency with clinical norms. As a result, multi-layer perceptron achieved 97.56% accuracy for sagittal, while linear support vector machine reached 90.24% for the vertical. Sagittal diagnoses showed average superiority (91.60 ± 5.43)% over the vertical (82.25 ± 6.37)%, where discriminative algorithms exhibited more steady performance (93.20 ± 3.29)% than the generative (85.98 ± 9.48)%. Further, the Kruskal-Wallis H test was carried out to explore statistical differences in diagnoses. Though sagittal patterns had no statistical difference in diagnostic accuracy, the vertical showed significance. All aspects of the tests indicated that the proposed craniofacial ML workflow was highly consistent with clinical norms and could supplement practical diagnosis., (© 2023. International Federation for Medical and Biological Engineering.)

Background Aims: Cell therapy is a promising treatment method that uses living cells to address a variety of diseases and conditions, including cardiovascular diseases, neurologic disorders and certain cancers. As interest in cell therapy grows, there is a need to shift to a more efficient, scalable and automated manufacturing process that can produce high-quality products at a lower cost., Methods: One way to achieve this is using non-invasive imaging and real-time image analysis techniques to monitor and control the manufacturing process. This work presents a machine learning-based image analysis pipeline that includes semantic segmentation and anomaly detection capabilities., Results/conclusions: This method can be easily implemented even when given a limited dataset of annotated images, is able to segment cells and debris and can identify anomalies such as contamination or hardware failure., Competing Interests: Declaration of Competing Interest The authors have no commercial, proprietary or financial interest in the products or companies described in this article., (Copyright © 2023 International Society for Cell & Gene Therapy. Published by Elsevier Inc. All rights reserved.)

The governance of cross-border data flows around the digital economy, data security, and data sovereignty has become a crucial global governance issue. This paper evaluates the legitimacy of data exit rules of CPTPP countries based on machine learning algorithm models under the perspective of cross-border data flow governance. In this study, four machine learning algorithms, namely, logistic regression, decision tree, random forest, and GBDT, are used to build an outbound data assessment and evaluation model. The confusion matrix is used to classify the outbound data legitimacy dichotomously. The recall, precision, and F1 scores are evaluated to compare the empirical results of each model. Based on this, a logistic regression-based outbound data risk scoring model is introduced to quantify the outbound data risk at a deeper level and to classify the outbound data risk level for the reference of regulators to make more scientific and reasonable decisions. The experimental results show that the machine learning models can meet the needs and applications of practical work and make accurate predictions of outbound data risks., Competing Interests: The authors declare that they have no known competing financial interests., (Copyright © 2022 Jing Li.)

With the continuous development and improvement of artificial intelligence technology, machine learning technology has also been extensively developed, which has promoted the development of computer vision, image processing, natural language processing, and other fields. Purpose . This article aims to apply the image processing technology based on machine learning in the detection of childhood diseases and propose the application of image processing technology to the detection of childhood diseases. This article introduces machine learning, image recognition technology, and related algorithms in detail and experiments on image recognition technology based on machine learning. The experimental results show that image recognition technology based on machine learning can well identify white blood cells that are difficult to distinguish with the naked eye, with a recognition rate of up to 90%. Applying image recognition technology based on machine learning in disease diagnosis has greatly improved the level of medical diagnosis., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Jian Huang et al.)

Objectives: Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms., Methods: The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model., Results: Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis., Conclusions: The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/ , which was valuable for individualized treatment decision before surgery., (© 2022. The Author(s).)

This study applies the Internet of things information-aware technology to the process of electronic market warehousing and logistics management, effectively perceives warehouse electronic product logistics information, and improves the real-time perception of electronic product logistics information and the efficiency of electronic product storage logistics management. This study first analyzes the needs of the intelligent electronic market warehouse logistics management system and then builds the IoT architecture of the intelligent warehouse logistics assembly logistics management system for electronic warehouses based on machine learning algorithms, which solves the problems that exist in the current workshop electronic market warehouse logistics management. Then, the principle of RFID technology is introduced. The accuracy of RFID tag estimation is analyzed by the PEPC tag estimation algorithm. It is concluded that the PEPC tag estimation algorithm reduces the tag estimation error and improves the accuracy of tag estimation. Finally, an intelligent warehousing logistics management system based on IoT RFID technology is established. The test results show that the system can meet the requirements of intelligent warehousing function in the electronic market, which will greatly improve the warehousing efficiency of electronic products., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 Ruifeng Zhang et al.)

Abstract: Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mart for Intensive Care III, we identified 604 and 1,614 patients, respectively. Two popular machine learning algorithms (i.e., extreme gradient boosting [XGBoost] and logistic regression) were applied. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models. By analyzing the feature importance based on XGBoost, we found that features in vital signs (VS), routine blood (RB), and blood gas analysis (BG) were the most relevant to THS (0.292, 0.249, and 0.225, respectively). Thus, the stepped relationships existing in them were revealed. Furthermore, the three stepped feature sets (i.e., VS, VS + RB, and VS + RB + sBG) were passed to the two machine learning algorithms to predict THS in the subsequent T hours (where T = 3, 2, 1, or 0.5), respectively. Results showed that the XGBoost model performance was significantly better than the logistic regression. The model using vital signs alone achieved good performance at the half-hour time window (AUROC = 0.935), and the performance was increased when laboratory results were added, especially when the time window was 1 h (AUROC = 0.950 and 0.968, respectively). These good-performing interpretable models demonstrated acceptable generalization ability in external validation, which could flexibly and rollingly predict THS T hours (where T = 0.5, 1) prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed THS prediction models., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society.)

A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study., (© 2021. The Author(s).)

Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information about human genome and machine learning, this paper discusses the effect of the fusion sequence and gene-level feature extraction on the accuracy of methylation site recognition. The significant limitation of existing computing tools was exposed by discovered of new features. (1) Most prediction models are based solely on sequence features and use SVM or random forest as classification methods. (2) Limited by the number of samples, the model may not achieve good performance. In order to establish a better prediction model for methylation sites, we must set specific weighting strategies for training samples and find more powerful and informative feature matrices to establish a comprehensive model. In this paper, we present HSM6AP, a high-precision predictor for the Homo sapiens N6-methyladenosine ( m 6 A ) based on multiple weights and feature stitching. Compared with existing methods, HSM6AP samples were creatively weighted during training, and a wide range of features were explored. Max-Relevance-Max-Distance (MRMD) is employed for feature selection, and the feature matrix is generated by fusing a single feature. The extreme gradient boosting (XGBoost), an integrated machine learning algorithm based on decision tree, is used for model training and improves model performance through parameter adjustment. Two rigorous independent data sets demonstrated the superiority of HSM6AP in identifying methylation sites. HSM6AP is an advanced predictor that can be directly employed by users (especially non-professional users) to predict methylation sites. Users can access our related tools and data sets at the following website: http://lab.malab.cn/~lijing/HSM6AP.html The codes of our tool can be publicly accessible at https://github.com/lijingtju/HSm6AP.git .

Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.

Background: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality., Objective: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission., Method: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix., Results: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai., Conclusion: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission., Competing Interests: Conflict of interest: G. Wu has nothing to disclose. Conflict of interest: P. Yang has nothing to disclose. Conflict of interest: Y. Xie has nothing to disclose. Conflict of interest: H.C. Woodruff owns shares in Oncoradiomics, outside the submitted work. Conflict of interest: X. Rao has nothing to disclose. Conflict of interest: J. Guiot has nothing to disclose. Conflict of interest: A-N. Frix has nothing to disclose. Conflict of interest: R. Louis has nothing to disclose. Conflict of interest: M. Moutschen has nothing to disclose. Conflict of interest: Jiawei Li has nothing to disclose. Conflict of interest: Jing Li has nothing to disclose. Conflict of interest: C. Yan has nothing to disclose. Conflict of interest: D. Du has nothing to disclose. Conflict of interest: S. Zhao has nothing to disclose. Conflict of interest: Y. Ding has nothing to disclose. Conflict of interest: B. Liu has nothing to disclose. Conflict of interest: W. Sun has nothing to disclose. Conflict of interest: F. Albarello has nothing to disclose. Conflict of interest: A. D'Abramo has nothing to disclose. Conflict of interest: V. Schininà has nothing to disclose. Conflict of interest: E. Nicastri has nothing to disclose. Conflict of interest: M. Occhipinti reports grants from Menarini Foundation and Novartis, outside the submitted work. Conflict of interest: G. Barisione has nothing to disclose. Conflict of interest: E. Barisione has nothing to disclose. Conflict of interest: I. Halilaj has nothing to disclose. Conflict of interest: P. Lovinfosse has nothing to disclose. Conflict of interest: X. Wang has nothing to disclose. Conflict of interest: J. Wu has nothing to disclose. Conflict of interest: P. Lambin reports, within the submitted work, minority shares in The Medical Cloud Company and, outside the submitted work, grants/sponsored research agreements from Varian medical, Oncoradiomics, ptTheragnostic/DNAmito and, Health Innovation Ventures; he received an advisor/presenter fee and/or reimbursement of travel costs/external grant writing fee and/or in kind manpower contribution from Oncoradiomics, BHV, Varian, Elekta, ptTheragnostic and Convert pharmaceuticals; P. Lambin has shares in the company Oncoradiomics SA, Convert pharmaceuticals SA and is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248, PCT/NL2014/050728) licensed to Oncoradiomics and one issued patent on mtDNA (PCT/EP2014/059089) licensed to ptTheragnostic/DNAmito, three non-patented invention (softwares) licensed to ptTheragnostic/DNAmito, Oncoradiomics and Health Innovation Ventures and three non-issues, non licensed patents on Deep Learning-Radiomics and LSRT (N2024482, N2024889, N2024889). Support statement: This work was supported from ERC advanced grant (ERC-ADG-2015, number 694812 – Hypoximmuno), European Program H2020 (ImmunoSABR – number 733008, PREDICT – ITN – number 766276, CHAIMELEON – number 952172, EuCanImage – number 952103), TRANSCAN Joint Transnational Call 2016 (JTC2016 “CLEARLY” – number UM 2017-8295), China Scholarships Council (number 201808210318), and Interreg V-A Euregio Meuse-Rhine (“Euradiomics” – number EMR4). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. This work was supported by the Dutch Cancer Society (KWF Kankerbestrijding), Project number 12085/2018-2. Funding information for this article has been deposited with the Crossref Funder Registry., (Copyright ©ERS 2020.)

Each influenza pandemic was caused at least partly by avian- and/or swine-origin influenza A viruses (IAVs). The timing of and the potential IAVs involved in the next pandemic are currently unpredictable. We aim to build machine learning (ML) models to predict human-adaptive IAV nucleotide composition. A total of 217,549 IAV full-length coding sequences of the PB2 (polymerase basic protein-2), PB1, PA (polymerase acidic protein), HA (hemagglutinin), NP (nucleoprotein), and NA (neuraminidase) segments were decomposed for their codon position-based mononucleotides (12 nts) and dinucleotides (48 dnts). A total of 68,742 human sequences and 68,739 avian sequences (1:1) were resampled to characterize the human adaptation-associated (d)nts with principal component analysis (PCA) and other ML models. Then, the human adaptation of IAV sequences was predicted based on the characterized (d)nts. Respectively, 9, 12, 11, 13, 10 and 9 human-adaptive (d)nts were optimized for the six segments. PCA and hierarchical clustering analysis revealed the linear separability of the optimized (d)nts between the human-adaptive and avian-adaptive sets. The results of the confusion matrix and the area under the receiver operating characteristic curve indicated a high performance of the ML models to predict human adaptation of IAVs. Our model performed well in predicting the human adaptation of the swine/avian IAVs before and after the 2009 H1N1 pandemic. In conclusion, we identified the human adaptation-associated genomic composition of IAV segments. ML models for IAV human adaptation prediction using large IAV genomic data sets can facilitate the identification of key viral factors that affect virus transmission/pathogenicity. Most importantly, it allows the prediction of pandemic influenza., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Background: Approximately 1 in 33 newborns is affected by congenital anomalies worldwide. We aimed to develop a practical model for identifying infants with a high risk of congenital cataracts (CCs), which is the leading cause of avoidable childhood blindness., Methods: This case-control study was performed in the Zhongshan Ophthalmic Center and involved 2005 subjects, including 1274 children with CCs and 731 healthy controls. The CC identification models were established based on birth conditions, family medical history, and family environmental factors using the random forest (RF) and adaptive boosting methods (trained by 1129 CC cases and 609 healthy controls), which were tested by internal 4-fold cross-validation and external validation (145 CC cases and 122 healthy controls). The models were also tested using 4 datasets with gradually reduced proportions of CC patients (bilateral cases) to validate their performance in an approximate simulation of a clinical environment with a relatively low disease prevalence., Findings: The CC identification models showed high discrimination in both the 4-fold cross validation (area under the curve (AUC)=0.91 [95% confidence interval: 0.88-0.94] in bilateral cases; 0.82 [0.77-0.89] in unilateral cases) and external validation (AUC=0.93±0.05 in bilateral cases; 0.86±0.01 in unilateral cases), and achieved stable performance in the clinical tests (AUC=0.94-0.96 in the four subgroups by RF). Furthermore, family history of CC, low parental education level, and comorbidity were identified as the top three most relevant factors to both bilateral and unilateral CC diagnosis., Interpretation: Our CC identification models can accurately discriminate CC patients from healthy children and have the potential to serve as a complementary screening procedure, especially in undeveloped and remote areas., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Although many machine learning algorithms have been developed to detect anterior cruciate ligament (ACL) injury based on magnetic resonance imaging (MRI), the performance of different algorithms required further investigation. The objectives of this current systematic review are to evaluate the diagnostic accuracy of machine-learning-assisted detection for ACL injury based on MRI and find the current best algorithm., Method: We will conduct a comprehensive database search for clinical diagnostic tests in PubMed, EMBASE, Cochrane Library, and Web of science without restrictions on publication status and language. The reference lists of the included articles will also be checked to identify additional studies for potential inclusion. Two reviewers will independently review all literature for inclusion and assess their methodological quality using Quality Assessment of Diagnostic Accuracy Studies version 2. Clinical diagnostic tests exploring the efficacy of machine-learning-assisted system for detecting ACL injury based on MRI will be considered for inclusion. Another 2 reviewers will independently extract data from eligible studies based on a pre-designed standardized form. Any disagreements will be resolved by consensus. RevMan 5.3 and Stata SE 12.0 software will be used for data synthesis. If appropriate, we will calculate the summary sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of machine-learning-assisted diagnosis system for ACL injury detection. A hierarchical summary receiver operating characteristic (HSROC) curve will also be plotted, and the area under the ROC curve (AUC) is going to calculated using the bivariate model. If the pooling of results is considered inappropriate, we will present and describe our findings in diagrams and tables and describe them narratively., Result: This is the first systematic assessment of machine learning system for the detection of ACL injury based on MRI. We predict it will provide highquality synthesis of existing evidence for the diagnostic accuracy of machine-learning-assisted detection for ACL injury and a relatively comprehensive reference for clinical practice and development of interdisciplinary field of artificial intelligence and medicine., Conclusion: This protocol outlined the significance and methodologically details of a systematic review of machine-learning-assisted detection for ACL injury based on MRI. The ongoing systematic review will provide high-quality synthesis of current evidence of machine learning system for detecting ACL injury., Registration: The meta-analysis has been prospectively registered in PROSPERO (CRD42019136581).

Background: Long noncoding RNAs (lncRNAs) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction accuracy and efficiency. In addition, the quick-update data of cancer, as well as the discovery of new mechanism, also underlay the possibility of improvement of cancer-related lncRNA prediction algorithm. In this study, we introduced CRlncRC, a novel Cancer-Related lncRNA Classifier by integrating manifold features with five machine-learning techniques., Results: CRlncRC was built on the integration of genomic, expression, epigenetic and network, totally in four categories of features. Five learning techniques were exploited to develop the effective classification model including Random Forest (RF), Naïve bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbors (KNN). Using ten-fold cross-validation, we showed that RF is the best model for classifying cancer-related lncRNAs (AUC = 0.82). The feature importance analysis indicated that epigenetic and network features play key roles in the classification. In addition, compared with other existing classifiers, CRlncRC exhibited a better performance both in sensitivity and specificity. We further applied CRlncRC to lncRNAs from the TANRIC (The Atlas of non-coding RNA in Cancer) dataset, and identified 121 cancer-related lncRNA candidates. These potential cancer-related lncRNAs showed a certain kind of cancer-related indications, and many of them could find convincing literature supports., Conclusions: Our results indicate that CRlncRC is a powerful method for identifying cancer-related lncRNAs. Machine-learning-based integration of multiple features, especially epigenetic and network features, had a great contribution to the cancer-related lncRNA prediction. RF outperforms other learning techniques on measurement of model sensitivity and specificity. In addition, using CRlncRC method, we predicted a set of cancer-related lncRNAs, all of which displayed a strong relevance to cancer as a valuable conception for the further cancer-related lncRNA function studies.

This paper presents a data-driven method to study the relationship of survival and clinical information of patients. The machine learning models were established to study the survival situation at the time of interest based on survival analysis. The way to determine the time of interest is an innovation of this paper. The distribution of survival time is considered, namely the three quartiles, as well as the traditional analysis experience is taken into consideration.

We carried out a series of statistical experiments to explore the utility of using relevance feedback on electroencephalogram (EEG) data to distinguish between different activity states in human absence epilepsy. EEG recordings from 10 patients with absence epilepsy are sampled, filtered, selected, and dissected from seizure-free, preseizure, and seizure phases. A total of 112 two-second 19-channel EEG epochs from 10 patients were selected from each phase. For each epoch, multiscale permutation entropy of the EEG data was calculated. The feature dimensionality was reduced by linear discriminant analysis to obtain a more discriminative and compact representation. Finally, a relevance feedback technique, that is, direct biased discriminant analysis, was applied to 68 randomly selected queries over nine iterations. This study is a first attempt to apply the statistical analysis of relevance feedback to the distinction of different EEG activity states in absence epilepsy. The average precision in the top 10 returned results was 97.5%, and the standard deviation suggested that embedding relevance feedback can effectively distinguish different seizure phases in absence epilepsy. The experimental results indicate that relevance feedback may be an effective tool for the prediction of different activity states in human absence epilepsy. The simultaneous analysis of multichannel EEG signals provides a powerful tool for the exploration of abnormal electrical brain activity in patients with epilepsy., (© EEG and Clinical Neuroscience Society (ECNS) 2014.)

As the core of health information technology (HIT), electronic medical record (EMR) systems have been changing to meet health care demands. To construct a new-generation EMR system framework with the capability of self-learning and real-time feedback, thus adding intelligence to the EMR system itself, this paper proposed a novel EMR system framework by constructing a direct pathway between the EMR workflow and EMR data. A prototype of this framework was implemented based on patient similarity learning. Patient diagnoses, demographic data, vital signs and structured lab test results were considered for similarity calculations. Real hospitalization data from 12,818 patients were substituted, and Precision @ Position measurements were used to validate self-learning performance. Our EMR system changed the way in which orders are placed by establishing recommendation order menu and shortcut applications. Two learning modes (EASY MODE and COMPLEX MODE) were provided, and the precision values @ position 5 of both modes were 0.7458 and 0.8792, respectively. The precision performance of COMPLEX MODE was better than that of EASY MODE (tested using a paired Wilcoxon-Mann-Whitney test, p < 0.001). Applying the proposed framework, the EMR data value was directly demonstrated in the clinical workflow, and intelligence was added to the EMR system, which could improve system usability, reliability and the physician's work efficiency. This self-learning mechanism is based on dynamic learning models and is not limited to a specific disease or clinical scenario, thus decreasing maintenance costs in real world applications and increasing its adaptability.

Bipolar disorder (BD) is a mental health condition characterized by large fluctuations in goal-directed energy and mood. BD is defined by the presence of at least one lifetime episode of mania, a prolonged period of excessive goal-directed behavior, hyperactivity and elevated mood. Previous computational models of BD have primarily focused on explaining mood fluctuations in mania, placing less emphasis on goal-directed symptoms. In this work, we use reinforcement learning (RL), a principled model of goal-directed behavior and learning, to show how augmenting RL agents with \textit{dynamic self-efficacy beliefs} can give rise to goal-directed and mood symptoms characteristic of the mania phase of BD. Our simulations demonstrate that a model-free RL agent that dynamically updates its self-efficacy beliefs learns optimistic overgeneralized value representations. We suggest that these representations may underlie several behaviors associated with mania, such as increased motivational drive and faster initiation of approach behavior (i.e. impatience). We further show that agents with more sensitive self-efficacy beliefs display increased willingness to exert effort in order to achieve higher goals even in the face of costs, a characteristic that is observed in individuals at risk for BD. Finally, unrealistically high self-efficacy beliefs that emerged with learning were accompanied by behaviors such as distractibility and compulsive action selection that have clinical parallels to symptoms of mania.

In this study, we investigate the impact of introducing enterprise relationship to train machine learning (ML) algorithms on improving its ability of forecasting small and medium-sized enterprises' (SMEs) credit risk in supply chain finance (SCF). First, we incorporate attributes of SME node in the CEs-SMEs network, a bipartite network constructed with transaction data in the Chinese automobile industry, into financial indicators to train ML algorithms for improving forecasting performance. Second, we employ the weighted one-mode projection approach to extract a projected-SMEs network that reveals the relationship among SMEs from the CEs-SMEs network and examine whether the ML algorithms' forecasting performances is improved when considering attributes in the projected-SMEs network. Third, we analyze the correlation between SME's credit risk and individual network attribute to specifically understand the impact of enterprise relationship on credit risk. Overall, the empirical results indicate that the forecasting performance is obviously better when enterprise transaction relationship is considered, and it is further enhanced after we apply weighted one-mode projection approach. Meanwhile, the variable importance analysis and the binomial logistic regression demonstrate how significantly each network attribute is correlated with credit risk, and the discussion on partial dependence plot shows that SMEs with large degree are non-risky generally. [ABSTRACT FROM AUTHOR]

Machine learning as a data‐driven solution has been widely applied in the field of fatigue lifetime prediction. In this paper, three models for wideband fatigue life prediction are built based on three machine learning models, that is, support vector regression (SVR), Gaussian process regression (GPR), and artificial neural network (ANN). All the three prediction models use the parameter b of the well‐known Tovo–Benasciutti (TB) model as their outputs to realize fatigue life prediction and their generalization abilities are enhanced by employing numerous power spectrum samples with different bandwidth parameters and a variety of material properties related to fatigue life. Sufficient Monte Carlo numerical simulations demonstrate that the newly developed machine learning models are superior to the traditional frequency‐domain models in terms of life prediction accuracy and the ANN model has the best overall performance among the three developed machine learning models. Highlights: Three frequency‐domain models are devised based on machine learning.SVR, GPR, and ANN models are employed for random fatigue life predictions.Three models are suitable for predicting fatigue life under wideband random loads.Three models outperform existing frequency‐domain models in prediction accuracy. [ABSTRACT FROM AUTHOR]

Wetlands, as a crucial component of terrestrial ecosystems, play a significant role in global ecological services. Aboveground biomass (AGB) is a key indicator of the productivity and carbon sequestration potential of wetland ecosystems. The current research methods for remote-sensing estimation of biomass either rely on traditional vegetation indices or merely perform integer-order differential transformations on the spectra, failing to fully leverage the information complexity of hyperspectral data. To identify an effective method for estimating AGB of mixed-wetland-plant communities, we conducted field surveys of AGB from three typical wetlands within the Crested Ibis National Nature Reserve in Hanzhong, Shaanxi, and concurrently acquired canopy hyperspectral data with a portable spectrometer. The spectral features were transformed by applying fractional-order differentiation (0.0 to 2.0) to extract optimal feature combinations. AGB prediction models were built using three machine learning models, XGBoost, Random Forest (RF), and CatBoost, and the accuracy of each model was evaluated. The combination of fractional-order differentiation, vegetation indices, and feature importance effectively yielded the optimal feature combinations, and integrating vegetation indices with feature bands enhanced the predictive accuracy of the models. Among the three machine-learning models, the RF model achieved superior accuracy using the 0.8-order differential transformation of vegetation indices and feature bands (R2 = 0.673, RMSE = 23.196, RPD = 1.736). The optimal RF model was visually interpreted using Shapley Additive Explanations, which revealed that the contribution of each feature varied across individual sample predictions. Our study provides methodological and technical support for remote-sensing monitoring of wetland AGB. [ABSTRACT FROM AUTHOR]

Long-term and high spatial resolution aerosol optical depth (AOD) data are essential for climate change detection and attribution. Global ground-based AOD observations are sparsely distributed, and satellite AOD retrievals have a low temporal frequency as well low accuracy before 2000 over land. In this study, AOD at 550 nm is derived from visibility observations collected at more than 5000 meteorological stations over global land regions from 1959 to 2021. The AOD retrievals (550 nm) of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua Earth observation satellite are used to train the machine learning model, and the ERA5 reanalysis boundary layer height is used to convert the surface visibility to AOD. Comparisons with an independent dataset (AERONET ground-based observations) show that the predicted AOD has a correlation coefficient of 0.55 at the daily scale. The correlation coefficients are higher at monthly and annual scales, which are 0.61 and 0.65, respectively. The evaluation shows consistent predictive ability prior to 2000, with correlation coefficients of 0.54, 0.66, and 0.66 at the daily, monthly, and annual scales, respectively. Due to the small number and sparse visibility stations prior to 1980, the global and regional analysis in this study is from 1980 to 2021. From 1980 to 2021, the mean visibility-derived AOD values over global land areas, the Northern Hemisphere, and the Southern Hemisphere are 0.177, 0.178, and 0.175, with a trend of - 0.0029 per 10 years, - 0.0030 per 10 years, and - 0.0021 per 10 years from 1980 to 2021. The regional means (trends) of AOD are 0.181 (- 0.0096 per 10 years), 0.163 (- 0.0026 per 10 years), 0.146 (- 0.0017 per 10 years), 0.165 (- 0.0027 per 10 years), 0.198 (- 0.0075 per 10 years), 0.281 (- 0.0062 per 10 years), 0.182 (- 0.0016 per 10 years), 0.133 (- 0.0028 per 10 years), 0.222 (0.0007 per 10 years), 0.244 (- 0.0009 per 10 years), 0.241 (0.0130 per 10 years), and 0.254 (0.0119 per 10 years) in Eastern Europe, Western Europe, Western North America, Eastern North America, Central South America, Western Africa, Southern Africa, Australia, Southeast Asia, Northeast Asia, Eastern China, and India, respectively. However, the trends decrease significantly in Eastern China (- 0.0572 per 10 years) and Northeast Asia (- 0.0213 per 10 years) after 2014, with the larger increasing trend found after 2005 in India (0.0446 per 10 years). The visibility-derived daily AOD dataset at 5032 stations over global land from 1959 to 2021 is available from the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Atmos.tpdc.300822) (Hao et al., 2023). [ABSTRACT FROM AUTHOR]

Inversion of magnetic basement interfaces in basins is essential for interpreting potential field data and studying geothermal resource distribution, as well as basin formation and evolution. This paper introduces a novel method for inverting magnetic basement interfaces using a random forest regression (RFR) algorithm that combines potential field processing and machine learning techniques. The method creates magnetic base interface models and corresponding magnetic anomaly data through the random midpoint displacement method and magnetic interface finite element forward simulation. These anomalies are then processed using techniques such as directional transformations, analytical continuation, spatial derivatives, and fractional transformations. Feature attributes are extracted, and Gini importance is utilized to measure the contributions of feature factors, identify effective features, and enhance algorithm efficiency. The validity and practicality of the method are demonstrated through the analysis of both idealized and noisy models. The proposed machine learning‐based approach is more intelligent, efficient, and accurately represents the relief of magnetic base interfaces. When applied to magnetic survey data in the Datong Basin, it produced a reliable basin base model that aligns with known structural information, paving the way for further research in magnetic interface inversion. Key Points: A machine learning method based on random forest regression algorithm is used to recover the relief of the magnetic basement interface by using high‐precision magnetic dataApplying potential field processing and transformation to feature extraction for machine learningThe proposed technique intelligently and efficiently restores the 3D relief of the magnetic basement interface, and it has been successfully implemented in the Datong Basin's Tianzhen‐Yanggao sag [ABSTRACT FROM AUTHOR]

The operating environment greatly influences the accuracy of power metering devices, resulting in variations and inconsistencies in measurement results across different working situations. A calibration model for power metering devices is proposed in this study, considering a range of environmental circumstances. The first step involves investigating the environmental conditions that impact the accuracy of power metering devices. The mutual information approach is utilized to identify environmental disturbances affecting device accuracy. A machine learning-driven symmetry attention Long Short-Term Memory (LSTM) network addresses measurement errors, capitalizing on the network's symmetry data knowledge. Ultimately, the efficacy of the suggested approach is substantiated through the utilization of performance indicators, namely, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the proposed method can effectively reduce the errors of the power measurement device in all quarters, and the error reduction effect is over 10% in the spring, which is better than other models, demonstrating exemplary performance in correcting the calibration errors of the power measurement device. [ABSTRACT FROM AUTHOR]

Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is limited by the high cost and time-consuming wet-laboratory experiments and bioinformatics analysis. To address this challenge, we downloaded multimodal data of 326 gastric cancer patients from The Cancer Genome Atlas, including histopathological images, clinical data and various molecular data. Using these data, we conducted a comprehensive analysis to investigate the relationship between TMB, clinical factors, gene expression and image features extracted from hematoxylin and eosin images. We further explored the feasibility of predicting TMB levels, i.e. high and low TMB, by utilizing a residual network (Resnet)-based deep learning algorithm for histopathological image analysis. Moreover, we developed a multimodal fusion deep learning model that combines histopathological images with omics data to predict TMB levels. We evaluated the performance of our models against various state-of-the-art methods using different TMB thresholds and obtained promising results. Specifically, our histopathological image analysis model achieved an area under curve (AUC) of 0.749. Notably, the multimodal fusion model significantly outperformed the model that relied only on histopathological images, with the highest AUC of 0.971. Our findings suggest that histopathological images could be used with reasonable accuracy to predict TMB levels in gastric cancer patients, while multimodal deep learning could achieve even higher levels of accuracy. This study sheds new light on predicting TMB in gastric cancer patients. [ABSTRACT FROM AUTHOR]

In this paper, we propose a stochastic variance reduction gradient method with adaptive step size, referred to as the SVRG‐New BB method, to solve the convex stochastic optimization problem. The method could be roughly viewed as a hybrid of the SVRG algorithm and a new BB step mechanism. Under the condition that the objective function is strongly convex, we provide the linear convergence proof of this algorithm. Numerical experiment results show that the performance of the SVRG‐New BB algorithm can surpass other existing algorithms if parameters in the algorithm are properly chosen. [ABSTRACT FROM AUTHOR]