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A Clinical AI-Based System (MoodMon) for Affective Disorders: Algorithm Development and Validation.

BACKGROUND: Psychiatry needs objective technological tools to address global staffing shortages, stigma, and other systemic challenges. An AI-based system for mood monitoring (MoodMon) was developed along with a mobile app for smartphones to detect changes in the mental state of individuals with major depressive disorder (MDD) and bipolar disorder (BD) based on acoustic features derived from speech signals. A long-term, naturalistic study of the MoodMon system represents a breakthrough in biomarker validation. OBJECTIVE: The aim of the study was to determine whether acoustic features are effe

Cow-level evaluation of plasma essential amino acid clusters and their association with lactating dairy cow performance and efficiency.

Studying changes in plasma EAA concentration and profile may provide a foundation for precision protein nutrition strategies for feeding dai … is study aimed to evaluate the clusters identified in the review of Letelier et al. (2022a) using a supervised machine learning model to determine whether the differences in performance and efficiencies between clusters were consistent when assessed with an independent empirical dataset. These data were from a study of 62 cows stratified in 4 lactation stages (early, mid-ear …

Multicenter Evaluation of Machine-Learning Continuous Pulse Rate Algorithm on Wrist-Worn Device.

INTRODUCTION: Though wrist-worn photoplethysmography (PPG) sensors play an important role in long-term and continuous heart rhythm monitoring, signals measured at the wrist are contaminated by more intense motion artifacts compared to other body locations. Machine learning (ML)-based algorithms can improve long-term pulse rate (PR) tracking but are associated with more stringent regulatory requirements when intended for clinical use. This study aimed to evaluate the accuracy of a digital health technology using wrist-worn PPG sensors and an ML-based algorithm to measure PR continuously. METHO

Machine Learning-Based Prediction of Long-Term Intraocular Pressure Fluctuations in Open-Angle Glaucoma.

OBJECTIVE: To investigate the predictability of long-term intraocular pressure (IOP) fluctuations in open-angle glaucoma eyes implanted with … ate regression was applied to predict long-term fluctuations based on short-term data. In addition, supervised machine learning with a Random Forest Classifier was used to predict long-term fluctuations from clinical, demographic, and IOP-derived features. For each horizon (273 or 364 days) and threshold (+2.0, +3.0, and +4.0 mmHg), changes in mean nyctohemeral IOP were calc …

A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning.

Background To assess the feasibility of a machine learning (ML) approach using radiomics features of perfusion defects on rest myocardial perfusion imaging (MPI) to detect the presence of hibernating myocardium. Methodology Data of patients who underwent 99mTc-sestamibi MPI and 18F-FDG PET/CT for myocardial viability assessment were retrieved. Rest MPI data were processed on ECToolbox, and polar maps were saved using the NFile PMap tool. The reference standard for defining hibernating myocardium was the presence of mismatched perfusion-metabolism defect with impaired myocardial contractility a

AI in Medical Questionnaires: Scoping Review.

UNLABELLED: This systematic review aimed to explore the current applications, potential benefits, and issues of artificial intelligence (AI) in medical questionnaires, focusing on its role in 3 main functions: assessment, development, and prediction. The global mental health burden remains severe. The World Health Organization reports that >1 billion people worldwide experience mental disorders, with the prevalence of depression and anxiety among children and adolescents at 2.6% and 6.5%, respectively. However, commonly used clinical questionnaires such as the Hamilton Depression Rating Scale

Dataset of quality assurance measurements of rhythmic movements.

A low-cost quantitative structured office measurement of movements in the extremities of people with Parkinson's disease [1,2] was performed … ast spectrum of rhythmic processes. The dataset is a suitable template for solving unsupervised and supervised machine learning algorithms. Readers may utilize this procedure to assure the quality of rhythmic processes by confirming the absence of deviations in rate and rhythm. Thus, this procedure provides the means to confirm the quality of the vast spectrum of rhythmic pr …

Machine Learning in Palliative Care: Scoping Review of Applications.

BACKGROUND: Palliative care is increasingly recognized as essential for an aging population and rising life-limiting illnesses. Machine learning (ML) has been widely applied in this field, primarily for prognostication. However, recent literature suggests broader applications that may enhance patient-centered care and optimize system-level processes. OBJECTIVE: This study aimed to map and summarize the evolving landscape of ML applications in palliative care through a scoping review, identifying how studies extend beyond mortality prediction into new domains, while assessing explainability, e

Effect of COVID-19 vaccination and booster on maternal-fetal outcomes: a retrospective cohort study.

BACKGROUND: COVID-19 in pregnant people increases the risk for poor maternal-fetal outcomes. However, COVID-19 vaccination hesitancy remains … 13 243; two doses only), and vaccinated unboosted with propensity score matching (n=4414). We built supervised machine learning classification models, which we used to determine which people were more likely to be vaccinated or boosted at delivery. The primary outcome was maternal SARS-CoV-2 infection. COVID-19 vaccination status at delivery, COVID-19-related health care, pr …

Virtual Reality-Based Treatment for Military Members and Veterans With Combat-Related Posttraumatic Stress Disorder: Protocol for a Multimodular Motion-Assisted Memory Desensitization and Reconsolidation Randomized Controlled Trial.

BACKGROUND: Military members are at elevated risk of operational stress injuries, including posttraumatic stress disorder (PTSD) and moral injury. Although psychotherapy can reduce symptoms, some military members may experience treatment-resistant PTSD. Multimodular motion-assisted memory desensitization and reconsolidation (3MDR) has been introduced as a virtual reality (VR) intervention for military members with PTSD related to military service. The 3MDR intervention incorporates exposure therapy, psychotherapy, eye movement desensitization and reconsolidation, VR, supportive counselling, an

Development of Data-Driven Models for Just-in-Time Digital Self-Management Advice to Improve Physical Functioning in Hip and Knee Osteoarthritis: Protocol for the e-cOAch Cross-Over Study.

BACKGROUND: Clinical guidelines recommend a stepped-care strategy for patients with hip and knee osteoarthritis that begins with nonoperative approaches, including education, pain medication, and self-care. However, the implementation of stepped-care remains limited. Digital self-management interventions have the potential to support patients in applying lifestyle advice and self-care strategies, but current tools often provide generic support without long-term continuity. Artificial intelligence offers new opportunities to deliver personalized, just-in-time self-management interventions for p

Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis.

BACKGROUND: A major challenge in using electronic health records (EHR) is the inconsistency of patient follow-up, resulting in right-censored outcomes. This becomes particularly problematic in long-horizon event predictions, such as autism and attention-deficit/hyperactivity disorder (ADHD) diagnoses, where a significant number of patients are lost to follow-up before the outcome can be observed. Consequently, fully supervised methods such as binary classification (BC), which are trained to predict observed diagnoses, are substantially affected by the probability of sufficient follow-up, leadi

Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity: Protocol for a Retrospective and Prospective Multicenter Cohort Study.

BACKGROUND: Cardiorespiratory fitness (CRF) is a key predictor of cardiovascular and other health-related diseases in individuals with obesity. CRF is most accurately assessed through maximal exercise testing with advanced gas-analysis equipment (maximum volume of oxygen [VO2max]); however, this approach is time-consuming, costly, and requires specialized expertise. Therefore, submaximal tests and self-reported physical activity levels have been used to develop predictive algorithms to estimate CRF, yet they often performed poorly in individuals with low CRF levels, such as patients with obesi

Assignment of the Biological Identity of Breast Masses in Contrast-enhanced Spectral Computed Tomography Examinations of Chest Carried Out in Clinical Routine Diagnostics.

BACKGROUND: Focal breast lesions are observed in up to 5.8% of CT examinations performed in female patients for a wide variety of indications not affecting the breast. To simplify and standardize the further procedure in the case of breast masses visualized by computed tomography (CT), an easy and robust diagnostic approach in assigning surely benign findings, uncertain findings and probably malignant findings is warranted. OBJECTIVE: We aimed (1) to determine the diagnostic gain of quantitative measurements of the iodine content in breast masses visualized in contrast-enhanced dual energy (D

Development and validation of peripheral blood DNA methylation signatures to predict response to biological therapy in adults with Crohn's disease (EPIC-CD): an epigenome-wide association study.

BACKGROUND: Biological therapeutics are widely used in Crohn's disease, with evidence of efficacy from randomised trials and real-world expe … aseline DNA methylation markers associated with response or non-response to treatment, we performed supervised machine learning through stability selected gradient boosting. In a post-hoc analysis, we compared our DNA methylation-based prediction model with clinical decision support tools (CDSTs). FINDINGS: We profiled the peripheral blood DNA methylome of 273 adults with C …

Stratifying IVF population endometria using a prognosis gradient independent of endometrial timing†.

STUDY QUESTION: Can the disrupted window of implantation (WOI) be stratified according to transcriptomic patterns associated with reproductive success in IVF patients undergoing HRT? SUMMARY ANSWER: There are four transcriptomic patterns independent of endometrial timing associated with a gradient of reproductive prognosis underlying different molecular pathomechanisms. WHAT IS KNOWN ALREADY: A molecular heterogeneous profile independent of endometrial timing has been discovered as a cause of implantation failure that disrupt the endometrial transcriptome in the mid-secretory phase. However,

Artificial intelligence for risk assessment and outcome prediction in malignant haematology.

Risk stratification is the backbone of clinical decision-making for patients with haematological malignancies. Models currently used in routine clinical practice often rely on static rules, conventional statistics and expert consensus. While pragmatic and accessible, these models fail to capture the complex biological interactions and patient heterogeneity, especially as data dimensionality grows given the availability of more comprehensive molecular analysis. Artificial intelligence (AI), particularly machine learning (ML), offers the potential to process such high-dimensional data, uncover l

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… Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning. Haematologica. 2023;108(3):690–704. 38. Eckardt JN, Röllig C, Metzeler K, Heisig P, Stasik S, Georgi JA, et al. … Diffuse large B‐cell lymphoma outcome prediction by gene‐expression profiling and supervised machine learning. Nat Med. 2002;8(1):68–74. 45. Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, Redd RA, et al. …

Ручная обработка против методов извлечения глубоких признаков для диагностики множественной скlerоза на основе МРТ.

Фон: Несмотря на значительные продвижения в анализе медицинских изображений, автоматический диагноз множественной скlerоза (МС) на основе магнитно-резонансной томографии (МРТ) остается сложной задачей из-за сложности 3D-данных мозга и вариабельности вида плоскостей поражений. Цель: В этой работе мы предлагаем эффективную и оптимизированную систему извлечения признаков для автоматического диагноза МС с использованием МРТ с весом FLAIR, T1 и T2. Предложенный подход улучшает Декимальные Дескрипторные Паттерны (DDP) за счет интеграции локальной информации градиента, создавая 3D-представление текст

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… Linear Discriminant Analysis (LDA) Linear Discriminant Analysis (LDA) is a supervised machine learning technique used for classification and dimensionality reduction [ 24 , 25 ]. … Logistic Regression Logistic Regression is a supervised machine learning technique used to predict a continuous outcome based on one or more input features [ 27 ]. …

Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.

Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accel

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… Miniaturisation of the biophotonic platforms for in vitro single cell interrogation of laser induced fluorescence may facilitate in vivo detection of RA disease phenotypes using supervised machine learning. … Unsupervised learning algorithms are linked to clustering of data whilst supervised machine learning algorithms are committed to the prediction of a categorical outcome or classification [114] . …

Возможности и препятствия для глубокого изучения биологии и медицины.

Глубокое обучение описывает класс алгоритмов машинного обучения, которые способны объединять исходные ресурсы в слои промежуточных характеристик. Эти алгоритмы недавно продемонстрировали впечатляющие результаты в различных областях. Биология и медицина являются богатыми данными дисциплинами, однако данные являются сложными и зачастую недопонятными. Поэтому методы глубокого обучения могут быть особенно подходящими для решения проблем в этих областях. Мы изучаем возможности углубленного изучения различных биомедицинских проблем - стационарная классификация, фундаментальные биологические процессы