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A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individuals recruited through an app-based platform. Individuals classified skin lesions from the International Skin Lesion Challenge 2018 into 7 different categories. There was a large dispersion in the geographical location, experience, training, and performance of the recruited individuals. We tested several wisdom of the crowd algorithms of varying complexity from a simple unweighted average to more complex Bayesian models that account for individual patterns of errors. Using a switchboard analysis, we observe that the best-performing algorithms rely on selecting top performers, weighting decisions by training accuracy, and take into account the task environment. These algorithms far exceed expert performance. We conclude by discussing the implications of these approaches for the development of medical AI.

Abstract Background Functional somatic symptoms and disorders are common and complex phenomena involving both bodily and brain processes. They pose major challenges across medical specialties. These disorders are common and have significant impacts on patients’ quality of life and healthcare costs. Main body We outline five problems pointing to the need for a new classification: (1) developments in understanding aetiological mechanisms; (2) the current division of disorders according to the treating specialist; (3) failure of current classifications to cover the variety of disorders and their severity (for example, patients with symptoms from multiple organs systems); (4) the need to find acceptable categories and labels for patients that promote therapeutic partnership; and (5) the need to develop clinical services and research for people with severe disorders. We propose ‘functional somatic disorders’ (FSD) as an umbrella term for various conditions characterised by persistent and troublesome physical symptoms. FSDs are diagnosed clinically, on the basis of characteristic symptom patterns. As with all diagnoses, a diagnosis of FSD should be made after considering other possible somatic and mental differential diagnoses. We propose that FSD should occupy a neutral space within disease classifications, favouring neither somatic disease aetiology, nor mental disorder. FSD should be subclassified as (a) multisystem, (b) single system, or (c) single symptom. While additional specifiers may be added to take account of psychological features or co-occurring diseases, neither of these is sufficient or necessary to make the diagnosis. We recommend that FSD criteria are written so as to harmonise with existing syndrome diagnoses. Where currently defined syndromes fall within the FSD spectrum – and also within organ system-specific chapters of a classification – they should be afforded dual parentage (for example, irritable bowel syndrome can belong to both gastrointestinal disorders and FSD). Conclusion We propose a new classification, ‘functional somatic disorder’, which is neither purely somatic nor purely mental, but occupies a neutral space between these two historical poles. This classification reflects both emerging aetiological evidence of the complex interactions between brain and body and the need to resolve the historical split between somatic and mental disorders.

Professional knowledge is highlighted as an important prerequisite of both medical doctors and teachers. Based on recent conceptions of professional knowledge in these fields, knowledge can be differentiated within several aspects. However, these knowledge aspects are currently conceptualized differently across different domains and projects. Thus, this paper describes recent frameworks for professional knowledge in medical and educational sciences, which are then integrated into an interdisciplinary two-dimensional model of professional knowledge that can help to align terminology in both domains and compare research results. The models' two dimensions differentiate between cognitive types of knowledge and content-related knowledge facets and introduces a terminology for all emerging knowledge aspects. The models' applicability for medical and educational sciences is demonstrated in the context of diagnosis by describing prototypical diagnostic settings for medical doctors as well as for teachers, which illustrate how the framework can be applied and operationalized in these areas. Subsequently, the role of the different knowledge aspects for acting and the possibility of transfer between different content areas are discussed. In conclusion, a possible extension of the model along a "third dimension" that focuses on the effects of growing expertise on professional knowledge over time is proposed and issues for further research are outlined.

The main goal of this study is to report on the number of words (vocabulary load) native and non-native readers of medical textbooks written in English need to know in order to be able to meet the lexical demands of this type of subject-specific (medical) texts. For estimating the vocabulary load of medical textbooks, a corpus comparison approach and some existing word lists, popular in ESP and EAP, were used. The present investigation aims to answer the following questions: (1) How many words are needed beyond the General Service List (GSL; West, 1953), the Academic Word List (AWL; Coxhead, 2000), and the EAP Science List (Coxhead and Hirsh, 2007) to achieve a good lexical text coverage? and (2) What is the vocabulary load of medical textbooks written in English? The implementation of this corpus comparison approach consisted of: (1) making a written medical corpus of 5.4 million tokens, (2) compiling a general written corpus of the same size (5.4 million tokens), (3) running both corpora (i.e., the medical and general) through some existing word lists (i.e., the GSL, the AWL, and the EAP Science List), and (4) creating new subject-specific (medical) word lists beyond the existing word lists used. The system for identifying medical words was based on Chung and Nation's (2003) criteria for classifying specialised vocabulary. The results of this investigation showed that there is a large number of subject-specific (medical) words in medical textbooks. For both native and non-native speakers of English training to be health professionals, this figure represents an enormous amount of vocabulary learning. This paper concludes by considering the value of creating specialised medical word lists for research, teaching and testing purposes.

Background. General Practice/Family Medicine includes approaches to the biological, technological, behavioural, sociological and anthropological domains. Objectives. To document the domains addressed in the final assignments of the Integrated Master's Degree in Medicine at the Faculty of Medicine, University of Coimbra, in the area of GP/FM. Material and methods. Observational study of the titles of final assignments, between 2008 and 2017, granted by the Faculty of Medicine of the University of Coimbra. A domain analysis using as codes the International Classification in Primary Care-2 and the Q-Codes, a context classification in Primary Care, year of elaboration and gender of author was carried out for each title of final assignment. A descriptive and inferential analysis was performed through parametric and nonparametric tests. Results. 169 papers were analysed, 23.1% written by male students, with a positive overall growth dynamics (Δ = +7) between 2008 and 2017. Q-Codes were registered 276 times, while the ICPC-2 codes were used 133 times. Under the Q-Codes, "doctor's issues" is the most frequently addressed (n = 112; 67.2%), and under the International Classification in Primary Care-2 classifications, the chapter "Psychological" was predominant (n = 35; 21%). Under the Q-Codes, subcategories "primary care setting" (n = 26; 15.6%), "health issue management" (n = 23; 13.8%) and "unable to code, unclear" (22; 13.2%) were dominant. Within the International Classification in Primary Care-2, the subcategories "diabetes noninsulin dependent" (n = 22; 13.2%), "depressive disorder" (8, 4.8%) and "hypertension uncomplicated" (8; 4.8%) were predominantly focused on. Conclusions. The 3CGP may become a professional tool, allowing for more precise identification of final works, for a better communication method in medical activity and for avoiding the loss of previously developed works. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction. [ABSTRACT FROM AUTHOR]

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

Background: Classifications of AAFD/PCFD have evolved with an increased understanding of the pathology involved. A review of classification systems helps identify deficiencies and respective contributions to the evolution in understanding the classification of AAFD/PCFD. Methods: Using multiple electronic database searches (Medline, PubMed) and Google search, original papers classifying AAFD/PCFD were identified. Nine original papers were identified that met the inclusion criteria. Results: Johnson’s original classification and multiple variants provided a significant leap in understanding and communicating the pathology but remained tibialis posterior tendon-focused. Drawbacks of these classifications include the implication of causality, linearity of progression through stages, an oversimplification of stage 2 deformity, and a failure to understand that multiple tendons react, not just tibialis posterior. Later classifications, such as the PCFD classification, are deformity-centric. Early ligament laxity/instability in normal attitude feet and all stages of cavus feet can present with pain and instability with minor/no deformity. These may not be captured in deformity-based classifications. The authors developed the ‘Triple Classification’ (TC) understanding that primary pathology is a progressive ligament failure/laxity that presents as tendon reactivity, deformity, and painful impingement, variably manifested depending on starting foot morphology. In this classification, starting foot morphology is typed, ligament laxities are staged, and deformity is zoned. Conclusions: This review has used identified deficiencies within classification systems for AAFD/PCFD to delink ligament laxity, deformity, and foot type and develop the ‘Triple classification’. Advantages of the TC may include representing foot types with no deformity, defining complex secondary instabilities, delinking foot types, tendon reactivity/ligament instability, and deformity to represent these independently in a new classification system. Level of Evidence: Level V.

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Abstract Brain tumors pose a serious threat to public health, impacting thousands of individuals directly or indirectly worldwide. Timely and accurate detection of these tumors is crucial for effective treatment and enhancing the quality of patients’ lives. The widely used brain imaging technique is magnetic resonance imaging, the precise identification of brain tumors in MRI images is challenging due to the diverse anatomical structures. This paper introduces an innovative approach known as the ensemble attention mechanism to address this challenge. Initially, the approach uses two networks to extract intermediate- and final-level feature maps from MobileNetV3 and EfficientNetB7. This assists in gathering the relevant feature maps from the different models at different levels. Then, the technique incorporates a co-attention mechanism into the intermediate and final feature map levels on both networks and ensembles them. This directs attention to certain regions to extract global-level features at different levels. Ensemble of attentive feature maps enabling the precise detection of various feature patterns within brain tumor images at both model, local, and global levels. This leads to an improvement in the classification process. The proposed system was evaluated on the Figshare dataset and achieved an accuracy of 98.94%, and 98.48% for the BraTS 2019 dataset which is superior to other methods. Thus, it is robust and suitable for brain tumor detection in healthcare systems.

Abstract The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

Abstract This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related to postnatal low back pain (LBP). This study applied regression and classification algorithms to the trunk movement proposed dataset from 100 postpartum women, 50 with LBP and 50 without. The Optimized optuna Regressor achieved the best regression performance with a mean squared error (MSE) of 0.000273, mean absolute error (MAE) of 0.0039, and R2 score of 0.9968. In classification, the Basic CNN and Random Forest Classifier both attained near-perfect accuracy of 1.0, the area under the receiver operating characteristic curve (AUC) of 1.0, precision of 1.0, recall of 1.0, and F1-score of 1.0, outperforming other models. Key predictive features included pain (correlation of -0.732 with flexion range of motion), range of motion measures (flexion and extension correlation of 0.662), and average movements (correlation of 0.957 with flexion). Feature selection consistently identified pain, flexion, extension, lateral flexion, and average movement as influential across methods. While limited to this initial dataset and constrained by generalizability, machine learning offered quantitative insight. Models accurately regressed (MSE 0.95) and classified (accuracy > 0.94) trunk biomechanics distinguishing LBP. Incorporating additional demographic, clinical, and patient-reported factors may enhance individualized risk prediction and treatment personalization. This preliminary application of advanced analytics supported machine learning's potential utility for both LBP risk determination and outcome improvement. This study provides valuable insights into the use of machine learning techniques for analyzing trunk movement in women with postnatal low back pain and can potentially inform the development of more effective treatments. Trial registration: The trial was designed as an observational and cross-section study. The study was approved by the Ethical Committee in Deraya University, Faculty of Pharmacy, (No: 10/2023). According to the ethical standards of the Declaration of Helsinki. This study complies with the principles of human research. Each patient signed a written consent form after being given a thorough description of the trial. The study was conducted at the outpatient clinic from February 2023 till June 30, 2023.

Abstract Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stage. Hence, clinical records may play a vital role to diagnose this disease at the correct stage for suitable medication to cure it. To detect lung cancer an accurate prediction method is needed which is significantly reliable. In the digital clinical record era with advancement in computing algorithms including machine learning techniques opens an opportunity to ease the process. Various machine learning algorithms may be applied over realistic clinical data but the predictive power is yet to be comprehended for accurate results. This paper envisages to compare twelve potential machine learning algorithms over clinical data with eleven symptoms of lung cancer along with two major habits of patients to predict a positive case accurately. The result has been found based on classification and heat map correlation. K-Nearest Neighbor Model and Bernoulli Naive Bayes Model are found most significant methods for early lung cancer prediction.

Background: Diabetes mellitus (DM) is a chronic condition that can lead to a variety of consequences. Diabetes is a condition that is caused by factors such as age, lack of exercise, sedentary lifestyle, family history of diabetes, high blood pressure, depression and stress, poor food, and so on. Diabetics are at a higher risk of developing diseases such as heart disease, nerve damage (diabetic neuropathy), eye problems (diabetic retinopathy), kidney disease (diabetic nephropathy), stroke, and so on. According to the International Diabetes Federation, 382 million people worldwide suffer from diabetes. By 2035, this number will have risen to 592 million. Every day, a large number of people become victims, and many are ignorant whether they have it or not. It primarily affects individuals between the ages of 25 and 74 years. If diabetes is left untreated and undiagnosed, it can lead to a slew of complications. The emergence of machine learning approaches, on the other hand, solves this crucial issue. Aims and Objectives: The aim was to study the DM and analyze how machine learning algorithms are used to identify the diabetes mellitus at an early stage, which is one of the most serious metabolic disorders in the world today. Methods and Materials: Data was obtained from databases such as Pubmed, IEEE xplore, and INSPEC,and from other secondary sources and primary sources in which methods based on machine learning approaches used in healthcare to predict diabetes at an early stage are reported. Results: After surveying various research papers, it was found that machine learning classification algorithms like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) etc shows the best accuracy for predicting diabetes at an early stage. Conclusion: Early detection of diabetes is critical for effective therapy. Many people have no idea whether or not they have it. The full assessment of Machine learning approaches for early diabetes prediction and how to apply a variety of supervised and unsupervised machine learning algorithms to the dataset to achieve the best accuracy are addressed in this paper.. Furthermore, the work will be expanded and refined to create a more precise and general predictive model for diabetes risk prediction at an early stage. Different metrics can be used to assess performance and for accurate diabetic diagnosis.

Background: During the process of the treatment of COVID-19 hospitalized patients, physicians still face a lot of unknowns and problems. Despite the application of the treatment protocol, it is still unknown why the medical status of a certain number of patients worsens and ends with death. Many factors were analyzed for the prediction of the clinical outcome of the patients using different methods. The aim of this paper was to develop a prediction model based on initial laboratory blood test results, accompanying comorbidities, and demographics to help physicians to better understand the medical state of patients with respect to possible clinical outcomes using neural networks, hypothesis testing, and confidence intervals. Methods: The research had retrospective-prospective, descriptive, and analytical character. As inputs for this research, 12 components of laboratory blood test results, six accompanying comorbidities, and demographics (age and gender) data were collected from hospital information system in Sarajevo for each patient from a sample of 634 hospitalized patients. Clinical outcome of the hospitalized patients, survival or death, was recorded 30 days after admission to the hospital. The prediction model was designed using a neural network. In addition, formal hypothesis tests were performed to investigate whether there were significant differences in laboratory blood test results and age between patients who died and those who survived, including the construction of 95% confidence intervals. Results: In this paper, 11 neural networks were developed with different threshold values to determine the optimal neural network with the highest prediction performance. The performances of the neural networks were evaluated by accuracy, precision, sensitivity, and specificity. Optimal neural network model evaluation metrics are: accuracy = 87.78%, precision = 96.37%, sensitivity = 90.07%, and specificity = 62.16%. Significantly higher values (P < 0.05) of blood laboratory result components and age were detected in patients who died. Conclusion: Optimal neural network model, results of hypothesis tests, and confidence intervals could help to predict, analyze, and better understand the medical state of COVID-19 hospitalized patients and thus reduce the mortality rate.

Abstract Genetic and agronomic advances consistently lead to an annual increase in global barley yield. Since abiotic stresses (physical environmental factors that negatively affect plant growth) reduce barley yield, it is necessary to predict barley resistance. Artificial intelligence and machine learning (ML) models are new and powerful tools for predicting product resilience. Considering the research gap in the use of molecular markers in predicting abiotic stresses, this paper introduces a new approach called GenPhenML that combines molecular markers and phenotypic traits to predict the resistance of barley genotypes to drought and salinity stresses by ML models. GenPhenML uses feature selection algorithms to determine the most important molecular markers. It then identifies the best model that predicts atmospheric resistance with lower MAE, RMSE, and higher R2. The results showed that GenPhenML with a neural network model predicted the salinity stress resistance score with MAE, RMSE and R2 values of 0.1206, 0.0308 and 0.9995, respectively. Also, the NN model predicted drought stress scores with MAE, RMSE and R2 values of 0.0727, 0.0105 and 0.9999, respectively. The GenPhenML approach was also used to classify barley genotypes as resistant and stress-sensitive. The results showed that the accuracy, accuracy and F1 score of the proposed approach for salinity and drought stress classification were higher than 97%.

Abstract The COVID-19 pandemic has had a significant impact on students’ academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits. Students getting used to online learning routines might find it even more challenging to perform well in face to face learning. Therefore, assessing various potential risk factors associated with students low performance and its prediction is important for early intervention. As students’ performance data encompass diverse behaviors, standard machine learning methods find it hard to get useful insights for beneficial practical decision making and early interventions. Therefore, this research explores regularized ensemble learning methods for effectively analyzing students’ performance data and reaching valid conclusions. To this end, three pruning strategies are implemented for the random forest method. These methods are based on out-of-bag sampling, sub-sampling and sub-bagging. The pruning strategies discard trees that are adversely affected by the unusual patterns in the students data forming forests of accurate and diverse trees. The methods are illustrated on an example data collected from university students currently studying on campus in a face-to-face modality, who studied during the COVID-19 pandemic through online learning. The suggested methods outperform all the other methods considered in this paper for predicting students at the risk of academic failure. Moreover, various factors such as class attendance, students interaction, internet connectivity, pre-requisite course(s) during the restrictions, etc., are identified as the most significant features.

Abstract Alzheimer’s disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Abstract As a preprocessing for machine learning and data mining, Feature Selection plays an important role. Feature selection aims to streamline high-dimensional data by eliminating irrelevant and redundant features, which reduces the potential curse of dimensionality of a given large dataset. When working with datasets containing many features, algorithms that aim to identify the most valuable features to improve dataset accuracy may encounter difficulties because of local optima. Many studies have been conducted to solve this problem. One of the solutions is to use meta-heuristic techniques. This paper presents a combination of the Differential evolution and the sailfish optimizer algorithms (DESFO) to tackle the feature selection problem. To assess the effectiveness of the proposed algorithm, a comparison between Differential Evolution, sailfish optimizer, and nine other modern algorithms, including different optimization algorithms, is presented. The evaluation used Random forest and key nearest neighbors as quality measures. The experimental results show that the proposed algorithm is a superior algorithm compared to others. It significantly impacts high classification accuracy, achieving 85.7% with the Random Forest classifier and 100% with the Key Nearest Neighbors classifier across 14 multi-scale benchmarks. According to fitness values, it gained 71% with the Random forest and 85.7% with the Key Nearest Neighbors classifiers.

Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.

Hybrid intelligent systems allow people to organize their time and simplify their day-to-day life processes related to forming a meal plan. This paper describes a method of presenting knowledge of the modern expert system, a description of the methods of the recommender system as a part of a hybrid intelligent system is given. Special attention is given to the legal regulation of intelligent systems in the area of telehealth. As a result of analyzing a number of properties of influencing people with inflammatory diseases of the digestive tract, a classified list of the main properties that affect patients with inflammatory diseases of the digestive system is highlighted. The paper presented the structure of the hybrid intelligent system for assisting people with gastrointestinal tract diseases. The paper also highlighted the main stages of designing such a system and presented the main algorithm of the system in general and of the process of forming a personalized nutrition plan. Besides, the process of making a diagnostics decision is described. A typical and alternative scenario of forming an individual nutrition plan was presented. The study considered an example of forming a nutrition plan based on the user's personal preferences. The study results are the recommendations for further development of the systems and a description of the ways of integrating the system in major telehealth services.

Aims: Long-term persistence of perfusion defect after pulmonaryembolism (PE) may lead to the development of chronic thromboembolic pulmonary hypertension. Identification of patients at risk of such a complication using a scoring system would be beneficial in clinical practice. Here, we aimed to derive a score for predicting persistence of perfusion defects after PE. Methods: 83 patients after PE were re-examined 6, 12 and 24 months after the PE episode. Data collected at the time of PE and perfusion status during follow-ups were used for modelling perfusion defects persistence using the Cox proportional hazards model and validated using bootstrap method. Results: A simple scoring system utilizing two variables (hemoglobin levels and age at the time of PE) was developed. Patients with hemoglobin levels over 140 g/L who were older than 65 years were at the highest risk of perfusion defects; in patients with the same hemoglobin levels and age

This paper presents the classification of electroencephalogram (EEG) signals using artificial neural network techniques. The signal processing of EEG signal could provide several areas for research in biomedical field. Numerous techniques can be applied to extract out the EEG characteristics in order to study and investigates the problems in the pattern recognition by its features extracted. The interesting site of signal measurement is the temporal lobe which is responsible of T3 and T4 in human electrode placement scalp. In this paper, many subjects were used to test the performance of non-neurophysiologic signals in order to investigate the electrical waves in human brain via the production of numerous EEG signals. A linear method of discrete wavelet transform (DWT) was used to gain classification with accuracy of 94.93% for testing EEG of different samples of music such as rock, jazz, classical and heavy metal using artificial neural network (ANN) with 2000 epoch, 25 nodes, 2 hidden layers. The results showed promisingly valuable EEG signal characteristics which could support the hospital staff to take care of and treat patients in the correct direction.

Analysis of long-term multichannel EEG signals for automatic seizure detection is an active area of research that has seen application of methods from different domains of signal processing and machine learning. The majority of approaches developed in this context consist of extraction of hand-crafted features that are used to train a classifier for eventual seizure detection. Approaches that are data-driven, do not use hand-crafted features, and use small amounts of patients' historical EEG data for classifier training are few in number. The approach presented in this paper falls in the latter category, and is based on a signal-derived empirical dictionary approach, which utilizes empirical mode decomposition (EMD) and discrete wavelet transform (DWT) based dictionaries learned using a framework inspired by traditional methods of dictionary learning. Three features associated with traditional dictionary learning approaches, namely projection coefficients, coefficient vector and reconstruction error, are extracted from both EMD and DWT based dictionaries for automated seizure detection. This is the first time these features have been applied for automatic seizure detection using an empirical dictionary approach. Small amounts of patients' historical multi-channel EEG data are used for classifier training, and multiple classifiers are used for seizure detection using newer data. In addition, the seizure detection results are validated using 5-fold cross-validation to rule out any bias in the results. The CHB-MIT benchmark database containing long-term EEG recordings of pediatric patients is used for validation of the approach, and seizure detection performance comparable to the state-of-the-art is obtained. Seizure detection is performed using five classifiers, thereby allowing a comparison of the dictionary approaches, features extracted, and classifiers used. The best seizure detection performance is obtained using EMD based dictionary and reconstruction error feature and support vector machine classifier, with accuracy, sensitivity and specificity values of 88.2, 90.3, and 88.1%, respectively. Comparison is also made with other recent studies using the same database. The methodology presented in this paper is shown to be computationally efficient and robust for patient-specific automatic seizure detection. A data-driven methodology utilizing a small amount of patients' historical data is hence demonstrated as a practical solution for automatic seizure detection.

Abstract A paper published in Orphanet Journal of Rare Diseases proposes a new classification of osteogenesis imperfecta (OI) based upon underlying pathological mechanisms. The proposed numbering of OI types conflicts with the currently used numbering and is likely to lead to confusion. In addition, classification of OI according to underlying pathogenic mechanisms is not novel.

In contrast with a conventional medical consultation, a "classical" homeopathic case taking usually ends up with the prescription of a remedy and not with a biomedical diagnostic, reflecting a specific homeopathic conceptualization of the human body, health and disease. This may be seen as one aspect of individualization in homeopathy, the approach through which the patient is not placed into a disease class but in which her/his unique features are taken into account when matching the symptoms with the symptomspicture of a remedy, the "similimum". In this paper, I examine the double orientation of homeopathic prescribing to individualization and classification. Drawing upon textual analysis of descriptions of remedies, interviews with patients and homeopaths, and observation of consultations and seminars, I show that individualization and classification are counterparts that cannot be meaningfully discussed if considered independently. My approach is based on treatment of the various encounters of patients and homeopaths as rhetorical situations. I argue that during the homeopathic consultation a process of construction and interpellation of the patient happens through various rhetorical moves. By examining them, I show how a sort of literature effect and a specific way of organizing knowledge in homeopathy simultaneously make the general to act on the particular while the particular or a sense of "it is about you" is also accomplished during the homeopathic consultation. [ABSTRACT FROM AUTHOR]

Abstract Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future.

Abstract Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance.

We propose a new pruning constraint when mining frequent temporal patterns to be used as classification and prediction features, the Semantic Adjacency Criterion [SAC], which filters out temporal patterns that contain potentially semantically contradictory components, exploiting each medical domain's knowledge. We have defined three SAC versions and tested them within three medical domains (oncology, hepatitis, diabetes) and a frequent-temporal-pattern discovery framework. Previously, we had shown that using SAC enhances the repeatability of discovering the same temporal patterns in similar proportions in different patient groups within the same clinical domain. Here, we focused on SAC's computational implications for pattern discovery, and for classification and prediction, using the discovered patterns as features, by four different machine-learning methods: Random Forests, Naïve Bayes, SVM, and Logistic Regression. Using SAC resulted in a significant reduction, across all medical domains and classification methods, of up to 97% in the number of discovered temporal patterns, and in the runtime of the discovery process, of up to 98%. Nevertheless, the highly reduced set of only semantically transparent patterns, when used as features, resulted in classification and prediction models whose performance was at least as good as the models resulting from using the complete temporal-pattern set. [ABSTRACT FROM AUTHOR]

Background Glaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus imaging serves as the principal method for diagnosing glaucoma and DR. Consequently, automated detection of eye diseases represents a significant application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) exhibits potential for effective eye disease detection. Methods This paper proposes the use of MATLAB – retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique was employed to retrain the AlexNet CNN for non-disease (Non_D), glaucoma (Sus_G) and diabetic retinopathy (Sus_R) classification. Moreover, model benchmarking was conducted using ResNet50 and GoogLeNet architectures. A Grad-CAM analysis is also incorporated for each eye condition examined. Results Metrics for validation accuracy, false positives, false negatives, precision, and recall were reported. Validation accuracies for the NetTransfer (I-V) and netAlexNet ranged from 89.7% to 94.3%, demonstrating varied effectiveness in identifying Non_D, Sus_G, and Sus_R categories, with netAlexNet achieving a 93.2% accuracy in the benchmarking of models against netResNet50 at 93.8% and netGoogLeNet at 90.4%. Conclusions This study demonstrates the efficacy of using a MATLAB-retrained AlexNet CNN for detecting glaucoma and diabetic retinopathy. It emphasizes the need for automated early detection tools, proposing CNNs as accessible solutions without replacing existing technologies.

Abstract Osteogenesis imperfecta (OI) is a connective tissue disorder affecting the skeleton and other organs, which has multiple genetic patterns, numerous causative genes, and complex pathogenic mechanisms. The previous classifications lack structure and scientific basis and have poor applicability. In this paper, we summarize and sort out the pathogenic mechanisms of OI, and analyze the molecular pathogenic mechanisms of OI from the perspectives of type I collagen defects(synthesis defects, processing defects, post-translational modification defects, folding and cross-linking defects), bone mineralization disorders, osteoblast differentiation and functional defects respectively, and also generalize several new untyped OI-causing genes and their pathogenic mechanisms, intending to provide the evidence of classification and a scientific basis for the precise diagnosis and treatment of OI.

Mixed-phenotype acute leukemia (MPAL) is a heterogeneous group of hematopoietic malignancies in which blasts show markers of multiple developmental lineages and cannot be clearly classified as acute myeloid or lymphoblastic leukemias. Historically, various names and classifications were used for this rare entity accounting for 2-5% of all acute leukemias depending on the diagnostic criterias used. The currently valid classification of myeloid neoplasms and acute leukemia published by the World Health Organization (WHO) in 2016 refers to this group of diseases as MPAL. Because adverse cytogenetic abnormalities are frequently present, MPAL is generally considered a disease with a poor prognosis. Knowledge of its treatment is limited to retrospective analyses of small patient cohorts. So far, no treatment recommendations verified by prospective studies have been published. The reported data suggest that induction therapy for acute lymphoblastic leukemia followed by allogeneic hematopoietic cell transplantation is more effective than induction therapy for acute myeloid leukemia or consolidation chemotherapy. The establishment of cooperative groups and international registries based on the recent WHO criterias are required to ensure further progress in understanding and treatment of MPAL. This review summarizes current knowledge on the diagnosis, classification, prognosis and treatment of MPAL patients.

Approximately 30%-40% of rare diseases are related to the endocrine and metabolic system, and abnormal metabolism of carbohydrate accounts for a significant proportion among others. Carbohydrate metabolic rare disorders often develop insidiously. The clinical symptoms of these disorders sometimes overlap with common diseases. Therefore, delayed diagnosis, misdiagnosis, and mismanagement happen often. The diagnosis and treatment of carbohydrate metabolic rare disorders is usually difficult in clinical practice. Efficient and practical screening models, identification of specific clinical features and biochemical changes, and genomic sequencing are critical to improve diagnostic efficiency. Most carbohydrate metabolic rare disorders are still lack in effective and targeted therapies. So, the symptomatic treatment is still main practice. The targeted medications and gene therapies based on precision diagnosis are directions for the diagnosis and management of rare disorders of carbohydrate metabolism in the future. In this paper, we classify the carbohydrate metabolic rare disorders based on their causes. We also discuss the current status and prospective of diagnosis and management of those diseases.

In the human body, white blood cells (WBCs) are crucial immune cells that help in the early detection of a variety of illnesses. Determination of the number of WBCs can be used to diagnose conditions such as hematological, immunological, and autoimmune diseases, as well as AIDS and leukemia. However, the conventional method of classifying and counting WBCs is time-consuming, laborious, and potentially erroneous. Therefore, this paper presents a computer-assisted automated method for recognizing and detecting WBC categories from blood images. Initially, the blood cell image is preprocessed and then segmented using an effective deep learning architecture called SegNet. Then, the important features are devised and extracted using the EfficientNet architecture. Finally, the WBCs are categorized into four different types using the XGBoost classifier: neutrophils, eosinophils, monocytes, and lymphocytes. The advantages of SegNet, EfficientNet, and XGBoost make the proposed model more robust and achieve a more efficient classification of the WBCs. The BCCD dataset is used to evaluate the performance of the proposed methodology, and the findings are compared to existing state-of-the-art approaches based on accuracy, precision, sensitivity, specificity, and F1-score. Evaluation results show that the proposed approach has a higher rank-1 accuracy of 99.02% and outperformed other existing techniques.

AbstractBackground The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional status of the esophagogastric junction (EGJ). Deep learning (DL) methods have been extensively employed in the area of digestive endoscopy. To improve the efficiency and accuracy of the endoscopist’s Hill classification and assist in incorporating it into routine endoscopy reports and GERD assessment examinations, this study first employed DL to establish a four-category model based on the Hill classification.Materials and Methods A dataset consisting of 3256 GEFV endoscopic images has been constructed for training and evaluation. Furthermore, a new attention mechanism module has been provided to improve the performance of the DL model. Combined with the attention mechanism module, numerous experiments were conducted on the GEFV endoscopic image dataset, and 12 mainstream DL models were tested and evaluated. The classification accuracy of the DL model and endoscopists with different experience levels was compared.Results 12 mainstream backbone networks were trained and tested, and four outstanding feature extraction backbone networks (ResNet-50, VGG-16, VGG-19, and Xception) were selected for further DL model development. The ResNet-50 showed the best Hill classification performance; its area under the curve (AUC) reached 0.989, and the classification accuracy (93.39%) was significantly higher than that of junior (74.83%) and senior (78.00%) endoscopists.Conclusions The DL model combined with the attention mechanism module in this paper demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists.

The detection and classification of traumatic brain injury (TBI) by medical professionals can vary due to subjectivity and differences in experience. Thus, a computational approach for detecting and classifying TBI would be invaluable for an objective diagnosis of this injury. In this review paper, various machine learning algorithms used to detect, classify, and predict the severity and outcomes of TBI in a clinical setting are discussed. The most promising of these algorithms is the convolutional neural network (CNN), which is highlighted in the review.

Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced learning in nursing classification datasets, various oversampling configurations are analyzed to improve the data quality prior to modeling. The decision trees built are based on three easily identifiable and clinically relevant pressure ulcer risk indicators: mobility, activity, and skin moisture. Additionally, this research introduces a novel tabular visualization method to enhance the usability of the decision trees in clinical practice. Thus, the primary aim of this approach is to provide nursing professionals with valuable insights for assessing the potential risk levels of pressure ulcers, which could support their decision-making and allow, for example, the application of suitable preventive measures tailored to each patient’s requirements. The interpretability of the models proposed and their performance, evaluated through stratified cross-validation, make them a helpful tool for nursing care in estimating the pressure ulcer risk level.

Over the last two decades, augmented reality (AR) has led to the rapid development of new interfaces in various fields of social and technological application domains. One such domain is medicine, and to a higher extent surgery, where these visualization techniques help to improve the effectiveness of preoperative and intraoperative procedures. Following this trend, this paper presents SLIMBRAIN, a real-time acquisition and processing AR system suitable to classify and display brain tumor tissue from hyperspectral (HS) information. This system captures and processes HS images at 14 frames per second (FPS) during the course of a tumor resection operation to detect and delimit cancer tissue at the same time the neurosurgeon operates. The result is represented in an AR visualization where the classification results are overlapped with the RGB point cloud captured by a LiDAR camera. This representation allows natural navigation of the scene at the same time it is captured and processed, improving the visualization and hence effectiveness of the HS technology to delimit tumors. The whole system has been verified in real brain tumor resection operations. [ABSTRACT FROM AUTHOR]

Background. Cervical cancer begins in superficial cells and over time can invade deeper tissues and surrounding tissues. This paper presents a creative idea of using an ensemble classification algorithm that improves the predictive performance of an artificial intelligence system based on cervical cancer screening. This study aimed to classify Pap-smear images by different machine learning methods to achieve high accuracy detection. Methods. This study was performed on 917 Pap-smear images from the Herlev public database. In the feature extraction stage, 20 geometric features and 76 texture features were extracted. After that, using ensemble classification method, the images were classified into two categories (i.e., normal and abnormal) and then into seven categories (i.e., superficial epithelial, intermediate epithelial, columnar epithelial, mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma) and the accuracy of the proposed method was evaluated. Results. The algorithm in the ensemble classification was able to achieve accuracy of 99.9% with a processing time of 0.028 second in the two-class classification and accuracy of 76.5% with a processing time of 0.033 second in the seven-class classification. Conclusion. Based on the results, the designed algorithm can be used as a computer aided diagnostic tool to increase the accuracy and speed of predicting the risk of cervical cancer. Practical Implications. Cervical cancer is one of the most common cancers among women. Early diagnosis of the disease can save various costs and prevent the patients’ frequent visits to medical centers. This research proposed an artificial intelligence method for automatic classification of cervical cells and improving the accuracy of diagnosis.

Auscultation methods enable non-invasive diagnosis of diseases, e.g. of the heart, based on heartbeat sounds. Regular, early examinations using machine learning techniques could help to detect diseases at an early stage to prevent serious health conditions and then provide optimal therapy through continuous monitoring. There is already a lot of work on human data using AI algorithms to detect patterns in signals or images. However, there is hardly no work on detecting heart murmurs with digital such as Myxomatous Mitral Valve Disease. In this paper, we present a canine auscultation project that aims to provide a tool to establish a baseline of classification parameters from audio signals that could be used to monitor canine health status by analyzing deviations from this baseline. In the future, data analysis could also lead to prediction and early detection of other diseases.

This article introduces a novel approach to digitize legislation using rule based-decision trees (RBDTs). As regulation is one of the major barriers to innovation, novel methods for helping stakeholders better understand, and conform to, legislation are becoming increasingly important. Newly introduced medical device regulation has resulted in an increased complexity of regulatory strategy for manufacturers, and the pressure on notified body resources to support this process is making this an increasing concern in industry. This paper explores a real-world classification problem that arises for medical device manufacturers when they want to be certified according to the In Vitro Diagnostic Regulation (IVDR). A modification to an existing RBDT algorithm is introduced (RBDT-1C) and a case study demonstrates how this method can be applied. The RBDT-1C algorithm is used to design a decision tree to classify IVD devices according to their risk-based classes: Class A, Class B, Class C and Class D. The applied RBDT-1C algorithm demonstrated accurate classification in-line with published ground-truth data. This approach should enable users to better understand the legislation, has informed policy makers about potential areas for future guidance, and allowed for the identification of errors in the regulations that have already been recognized and amended by the European Commission.

Despite their relevance in neurorehabilitation, physical therapy (PT) goals and interventions are poorly described, compromising a proper understanding of PT effectiveness in everyday clinical practice. Thus, this paper aims to describe the prevalence of PT goals and interventions in people with neurological disorders, along with the participants’ clinical features, setting characteristics of the clinical units involved, and PT impact on outcome measures. A multicenter longitudinal observational study involving hospitals and rehabilitation centers across Italy has been conducted. We recruited people with stroke (n = 119), multiple sclerosis (n = 48), and Parkinson’s disease (n = 35) who underwent the PT sessions foreseen by the National Healthcare System. Clinical outcomes were administered before and after the intervention, and for each participant the physical therapists completed a semi-structured interview to report the goals and interventions of the PT sessions. Results showed that the most relevant PT goals were related to the ICF activities with “walking” showing the highest prevalence. The most used interventions aimed at improving walking performance, followed by those aimed at improving organ/body system functioning, while interventions targeting the cognitive–affective and educational aspects have been poorly considered. Considering PT effectiveness, 83 participants experienced a clinically significant improvement in the outcome measures assessing gait and balance functions.

Detecting Alzheimer's disease (AD) and disease progression based on the patient's speech not the patient's speech data can aid non-invasive, cost-effective, real-time early diagnostic and repetitive monitoring in minimum time and effort using machine learning (ML) classification approaches. This paper aims to predict early AD diagnosis and evaluate stages of AD through exploratory analysis of acoustic features, non-stationarity, and non-linearity testing, and applying data augmentation techniques on spontaneous speech signals collected from AD and cognitively normal (CN) subjects. Evaluation of the proposed AD prediction and AD stages classification models using Random Forest classifier yielded accuracy rates of 82.2% and 71.5%. This will enrich the Alzheimer's research community with further understanding of methods to improve models for AD classification and addressing non-stationarity and non-linearity properties on audio features to determine the best-suited acoustic features for AD monitoring.

Chiari malformations are a group of craniovertebral junction anomalies characterized by the herniation of cerebellar tonsils below the foramen magnum, often accompanied by brainstem descent. The existing classification systems for Chiari malformations have expanded from the original four categories to nine, leading to debates about the need for a more descriptive and etiopathogenic terminology. This review aims to examine the various classification approaches employed and proposes a simplified scheme to differentiate between different types of tonsillar herniations. Furthermore, it explores the most appropriate terminology for acquired herniation of cerebellar tonsils and other secondary Chiari-like malformations. Recent advances in magnetic resonance imaging (MRI) have revealed a higher prevalence and incidence of Chiari malformation Type 1 (CM1) and identified similar cerebellar herniations in individuals unrelated to the classic phenotypes described by Chiari. As we reassess the existing classifications, it becomes crucial to establish a terminology that accurately reflects the diverse presentations and underlying causes of these conditions. This paper contributes to the ongoing discussion by offering insights into the evolving understanding of Chiari malformations and proposing a simplified classification and terminology system to enhance diagnosis and management.

Abstract Oscar Nerval de Gouvêa was a scientist and teacher in Rio de Janeiro, Brazil, whose work spanned engineering, medicine, the social sciences, and law. This paper presents and discusses a manuscript entitled “Table of mineral classification,” which he appended to his dissertation Da receptividade mórbida , presented to the Faculty of Medicine in 1889. The foundations and features of the table provide a focus for understanding nineteenth-century mineralogy and its connections in Brazil at that time through this scientist. This text was Gouvêa’s contribution to the various mineral classification systems which have emerged from different parts of the world.

Breast cancer is one of the most common cancers in women all over the world. Due to the improvement of medical treatments, most of the breast cancer patients would be in remission. However, the patients have to face the next challenge, the recurrence of breast cancer which may cause more severe effects, and even death. The prediction of breast cancer recurrence is crucial for reducing mortality. This paper proposes a prediction model for the recurrence of breast cancer based on clinical nominal and numeric features. In this study, our data consist of 1,061 patients from Breast Cancer Registry from Shin Kong Wu Ho-Su Memorial Hospital between 2011 and 2016, in which 37 records are denoted as breast cancer recurrence. Each record has 85 features. Our approach consists of three stages. First, we perform data preprocessing and feature selection techniques to consolidate the dataset. Among all features, six features are identified for further processing in the following stages. Next, we apply resampling techniques to resolve the issue of class imbalance. Finally, we construct two classifiers, AdaBoost and cost-sensitive learning, to predict the risk of recurrence and carry out the performance evaluation in three-fold cross-validation. By applying the AdaBoost method, we achieve accuracy of 0.973 and sensitivity of 0.675. By combining the AdaBoost and cost-sensitive method of our model, we achieve a reasonable accuracy of 0.468 and substantially high sensitivity of 0.947 which guarantee almost no false dismissal. Our model can be used as a supporting tool in the setting and evaluation of the follow-up visit for early intervention and more advanced treatments to lower cancer mortality.

The paper focuses on results of substantiating and selecting informative physiological criteria that can be used for assessing and controlling functional state and working conditions category taking into account physical and nervous-emotional loads borne by CNC- machinery operators. Basing on complex physiological and ergonomic studies and retrospective data analysis, we showed that workers from various occupational groups who dealt with physical labor had to face certain strain over a working shift. Such strains, given long-term working experience, could result in neuromuscular system overstrain and occupational diseases occurrence. We substantiated and developed informative physiological criteria that allowed assessing and controlling functional state and working capacity as well as working conditions category taking into account occupational activities. The present research involved using a set of occupational studies, physiological and ergonomic procedures as well as clinical and statistic ones for examining peculiarities related to functional state of workers’ bodies under exposure to occupational factors taking into account specific working tasks and loads. It allowed us to substantiate labor intensity assessment. Our research results revealed that there was a strong correlation between hand muscles endurance to static exertion (decrease in % by the end of a work shift) and working conditions category given local and overall muscular loads borne by workers. This criterion is recommended for control over functional state and working capacity taking into account occupational peculiarities and gender-related differences. It is necessary to accumulate scientific data for confirming a similar correlation between overall physical working capacity (OPWC) and working conditions category. Results obtained via physiological research were used for developing prevention activities for workers.

Access to digital health data collections such as clinical notes, discharge summaries, or medical charts has increased in the last few years due to the increased use of electronic health records, which provide instant access to patients’ clinical information. The volume and the unstructured nature of these datasets present great challenges in analyses and subsequent applications to healthcare. The growing volume of clinical data generated and stored in electronic health records creates challenges for physicians when reviewing patients’ records with the aim of understanding individual patients’ health histories. Electronic healthcare records contain large volumes of unstructured data, which require one to read through to get the required information. This is a challenging task due to lack of suitable techniques to quickly extract the needed information. Information processing tools in the clinical domain that provide support to users in seeking needed information are lacking. The use of data visualization has been introduced in an attempt to solve this problem; however, no single approach has been widely adopted. In this paper, we propose a unique approach for modeling clinical notes using the semantics of various units of a clinical text document to aid doctors in reviewing electronic clinical notes. This is achieved by applying the supervised machine learning technique to identify and present semantically similar information together, facilitating the identification of relevant information to users.

In this paper, a deep learning approach for classification of cough sound segments is presented. The architecture of the network is based on a pre-trained network and the spectrogram images of three recording channels have been extracted for the sake of training the network. The classification accuracy based on three recording channels is 92% for a binary classification model and the network converges fast. Two classification models based on binary and multi-class problems are proposed. Relevant classification parameters including the Receiver Operating Characteristic (ROC) curve are reported.