Obesity

Comparison of ChatGPT and dietitians in formulating diet plans and recommendations for patients with cardiometabolic diseases.

BACKGROUND: Artificial Intelligence (AI) is transforming personalized medicine, yet its efficacy constitutes a dynamic factor in the field of health and personalized medicine. OBJECTIVE: This study compared ChatGPT's ability to construct dietetic plans and provide nutritional advice against professional dietitians. METHODS: Three dietitians and ChatGPT generated diet plans and gave recommendations...

Hybrid deep learning model for brain age prediction using time-distributed convolutional and bidirectional LSTM networks.

Brain age prediction has gained significant attention due to its strong correlation with neurological and cognitive disorders. The discrepancy between an individual's chronological age and their predicted brain age-known as the Brain Age Gap-has been linked to conditions such as schizophrenia, Alzheimer's disease, cognitive decline, and lifestyle factors like stress and poor health. A positive Bra...

Discriminating HFrEF vs HFpEF from chest radiographs: Mitigating demographic performance gaps via augmentation and multimodal fusion.

Deep learning models applied to chest radiography have shown promise for cardiac phenotyping, yet concerns remain regarding demographic performance di...

Fat matters: paraspinal muscle quality is associated with electromyographic fatigability and endurance in lumbar spinal stenosis.

BACKGROUND: Paraspinal muscle degeneration with fatty infiltration and atrophy is well-documented in symptomatic lumbar spinal stenosis (sLSS). Howeve...

Spinal cord FDG PET uptake in healthy adults: an AI-driven segmentation study.

PURPOSE: 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET)/Computerized Tomography (CT) is an important imaging modality in oncology, bu...

AI-Powered Resting 12-Lead Electrocardiogram Algorithm for Predicting Low Peak Oxygen Consumption: Development and Validation Study.

BACKGROUND: Low peak oxygen consumption (V̇O2) is associated with higher cardiovascular and all-cause mortality, while improvements in peak V̇O2 reduc...

Interpretable machine learning for endometriosis classification: a rule-based approach.

BACKGROUND: Endometriosis is a chronic gynecological disease characterized by the growth of endometrial-like tissue outside the uterus, leading to pel...

Predicting poor health-related quality of life among Chinese adolescents using explainable machine learning: the role of school adjustment, family context, and lifestyle factors.

BACKGROUND: Adolescents' health-related quality of life (HRQoL) is influenced by multiple domains, including school, family, and individual lifestyle....

Identifying PLAU as a shared pathogenic gene in type 2 diabetes and bladder urothelial carcinoma through integrated transcriptomic analysis and machine learning for diagnostic and therapeutic value.

Type 2 diabetes mellitus (T2DM) and bladder urothelial carcinoma (BLCA) are two kinds of diseases that seriously threaten human health. Their pathogen...

Multi-Omics Integration Reveals the Genetic Mechanisms of Periodontitis and Predicts Therapeutic Drugs.

BACKGROUND: Periodontitis is a chronic inflammatory disease driven by host immune dysregulation. However, the specific genetic regulatory mechanisms u...

Biomarker-driven diagnostics and personalized treatment in polycystic ovary syndrome.

Polycystic Ovary Syndrome (PCOS) is a common and clinically heterogeneous endocrine disorder affecting approximately 4-20% of women worldwide. Its com...

A scoping review on aging and cardiovascular diseases - Molecular mediators and artificial intelligence-based advanced diagnostic methods.

BACKGROUND: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, and their risk increases with age. Biological age reflects...

Prediction of ICU length of stay, hospital discharge outcomes and discharge location among ICU-admitted patients diagnosed with viral hepatitis using machine learning: a retrospective cohort study of the MIMIC-IV database.

BACKGROUND: Hepatitis, a disease characterised by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million...

Development and external validation of a machine learning model for predicting post-CABG acute kidney injury: insights from MIMIC database and real-world cardiac surgery cohort.

OBJECTIVE: Acute kidney injury (AKI) is a severe complication following coronary artery bypass grafting(CABG) While machine learning models trained on...

Wave-aware mortality prediction in COVID-19: a multi-stage feature selection and explainable machine-learning framework.

BACKGROUND: Mortality among hospitalized COVID-19 patients changed markedly across successive epidemic waves as circulating variants shifted, populati...

Age-stratified risk profiles for emergency colorectal cancer resection: A machine-learning analysis.

BACKGROUND: Emergency colorectal cancer resection (ECCR) is associated with worse perioperative and oncologic outcomes than elective surgery. As the i...

Interpretable Machine Learning to Predict Anxiety or Depression in Acute Ischemic Stroke after IV Thrombolysis: A Retrospective Cohort Study.

BACKGROUND: Acute Ischemic Stroke (AIS) represents a prevalent cerebrovascular condition characterized by significant levels of disability and mortali...

Development and Validation of a Novel Risk Prediction Model for Incident Diabetic Foot Complications: A Large, Longitudinal, Population-Based Machine Learning Study.

OBJECTIVE: Diabetic foot complications (DFCs) are common diabetes complications. Existing tools for predicting incident DFCs remain insufficient. This...

An explainable predictive machine learning model of oxaliplatin induced peripheral neuropathy based on clinical data: a retrospective single center.

BACKGROUND: Oxaliplatin (OXA) is widely used in the treatment of gastrointestinal malignancies such as colorectal cancer. However, oxaliplatin-induced...

Exploration of comorbidity mechanisms between chronic pain and depression: Machine learning prediction models and SHAP interpretability analysis based on the CHARLS cohort.

INTRODUCTION: With rapid population aging in China, depression among middle-aged and older adults has become a major public health concern. Chronic pa...