Obesity

Cross-platform metabolomics imputation using importance-weighted autoencoders

Metabolomics data are often generated through different analytical platforms and different methods of identification and quantification which makes their synthesis and large-scale replication challenging. To address this, we applied generative deep learning to impute metabolites assayed by Metabolon, a commonly used commercial platform, using metabolomic features acquired by an untargeted liquid c...

Optimized Feature Selection and Advanced Machine Learning for Stroke Risk Prediction in Revascularized Coronary Artery Disease Patients

Coronary artery disease (CAD) is a leading cause of mortality, with stroke being a major complication following coronary revascularization procedures such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). While machine learning (ML) has been used to predict postoperative outcomes, a gap remains in quantifying stroke risk in revascularized CAD patients. This st...

Paving the way for precision treatment of psychiatric symptoms with functional connectivity neurofeedback

Major depressive disorder (MDD) remains challenging to treat, with many patients failing to respond adequately to existing therapies. Patients with MD...

A Prospective Cohort Study to Develop Multi-Biomarkers Panel to Define Biological Ageing in Five Different Cohorts from Newborn to Oldest Adult: A Study Protocol

Age-associated disease management depends significantly on chronological age and macro-level clinical data sets. However, the biological age captures ...

A Combined Predictive and Causal Approach for Neighborhood-Level Diabetes Detection

Develop a neighborhood-level framework using machine learning and causal inference to identify socioeconomic and behavioral drivers of Type 2 diabetes...

Neuroimaging-AI endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders

Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotyp...

The gSOS Polygenic Score is Associated with Bone Density and Fracture Risk in Childhood

The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using machine learning algorithms in adults of European a...

TOWARDS AN AI-DRIVEN REGISTRY FOR POSTOPERATIVE COMPLICATIONS: A PROOF-OF-CONCEPT STUDY EVALUATING THE OPPORTUNITIES AND CHALLENGES OF AI-MODELS

Continuous quality improvement is essential in surgery, with clinical registries and quality improvement programs (QIPs) playing a key role. Postopera...

Reliable Radiologic Skeletal Muscle Area Assessment – A Biomarker for Cancer Cachexia Diagnosis

Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Mon...

Predicting Alzheimer’s Trajectory: A Multi-PRS Machine Learning Approach for Early Diagnosis and Progression Forecasting

Predicting the early onset of dementia due to Alzheimer’s Disease (AD) has major implications for timely clinical management and outcomes. Current dia...

Extracting Carotid Stenosis Severity from Clinical Notes Using Natural Language Processing: Development, Validation, and Application in a Nationwide Veteran Cohort

Carotid stenosis, which is atherosclerotic narrowing of the extracranial carotid arteries, is an important risk factor for ischemic stroke. The preval...

Machine learning prediction for early-stage melanoma outcomes: recurrence-free survival, disease-specific survival, and overall survival

This study compared machine-learning models for predicting recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) ...

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting

This study aimed to identify prognostic factors associated with poor outcomes of COVID-19 at diagnosis in Primary Health Care (PHC). We conducted a re...

Key predictors of maternal mild depression and anxiety in low resource settings: A machine learning approach

Maternal mental health (MMH) disorders, particularly depression and anxiety, are major public health concerns in low- and middle-income countries (LMI...

Status and Opportunities of Machine Learning Applications in Obstructive Sleep Apnea: A Narrative Review

Obstructive sleep apnea (OSA) is a prevalent and potentially severe sleep disorder characterized by repeated interruptions in breathing during sleep. ...

DeepDrug2: A Germline-focused Graph Neural Network Framework for Alzheimer’s Drug Repurposing Validated by Electronic Health Records

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with limited therapeutic options. The original DeepDrug framework by Li et al. (2025)...

Automated Deep Learning Pipeline for Characterizing Left Ventricular Diastolic Function

Left ventricular diastolic dysfunction (LVDD) is most commonly evaluated by echocardiography. However, without a sole identifying metric, LVDD is asse...

Dense sampling of choices links high learning rates to obesity and low reward sensitivity to binge eating

Mounting evidence shows that obesity is associated with alterations in dopamine transmission. However, in humans, corresponding changes in dopamine-de...

Machine Learning Identifies Microbiome and Clinical Predictors of Sustained Weight Loss Following Prolonged Fasting

Prolonged fasting may benefit metabolic health, but data in healthy individuals remain limited. We performed a randomized, waitlist-controlled study (...

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s Disease

Dementia, especially Alzheimer’s disease (AD), is a major global health challenge marked by progressive cognitive impairment, behavioral changes, and ...