Obesity

Machine learning for mortality prediction using nutritional and inflammatory markers in critically ill patients.

INTRODUCTION: Malnutrition and muscle loss are key determinants of outcomes in critically ill patients, yet conventional ICU mortality scores (e.g., APACHE II, SOFA) do not incorporate nutritional status. This study aimed to develop a machine learning model integrating clinical and nutritional indicators associated with malnutrition diagnosis to predict in-hospital mortality in critically ill pati...

Differential Factors Associated With the Presence of Persistent Symptoms in Individuals Diagnosed With Long COVID: Protocol for a Longitudinal Matched Case-Control Study.

BACKGROUND: Long COVID (postacute sequelae of SARS-CoV-2 infection) is a heterogeneous condition with persistent multisystem symptoms and substantial functional burden. Integrative longitudinal studies combining clinical phenotyping, lifestyle factors, and immunobiological markers are needed to clarify determinants of symptom persistence and inform risk stratification and targeted interventions. O...

Ameliorative effect of Rhus verniciflua Stokes on high-fat diet-induced obesity and advanced analysis with machine learning and deep learning-based network pharmacology.

AIMS: Obesity and metabolic dysfunction-associated steatotic liver disease (MASLD) arise from impaired redox and energy homeostasis, yet current thera...

Using artificial intelligence to predict sexual health outcomes in endometriosis: a decision tree model algorithm.

BACKGROUND: Endometriosis profoundly impairs sexual function through complex interactions between pain, hormonal disturbances, psychological distress,...

Extubation Decision Support in Critical Care: A Multimodal Machine Learning Framework Integrating Segmented Radiographs and Routine Clinical Data.

Extubation failure remains a major challenge in critically ill patients and is associated with adverse clinical outcomes. Current extubation decisions...

Severe COVID-19 in the Republic of Korea: Epidemiology, Risk Factors, Therapeutics, and Prognostic Models From Nationwide Data.

Severe coronavirus disease 2019 (COVID-19) has posed ongoing clinical and public health challenges worldwide, with Korea providing a unique perspectiv...

FTO-CHRM3 axis regulates multiple myeloma progression: a machine learning-based identification.

BACKGROUND: Fat Mass and Obesity-Associated (FTO) is linked to multiple myeloma (MM) progression, but its action mechanisms are poorly understood. MET...

Explainable Machine Learning for Assessing Digital Health Literacy in Older Adults: Validation and Development of a Two-Stage Model Integrating Performance-Based and Self-Assessed Indicators.

BACKGROUND: Digital health literacy (DHL) is the ability to locate, understand, evaluate, and apply health information in digital environments. It is ...

Combined variations in sleep, physical activity, and nutrition and the risk of major adverse cardiovascular events.

AIMS: Sleep, physical activity, and nutrition (SPAN) are major lifestyle behaviours that influence cardiovascular disease risk. We examined the multi-...

Development and validation of a risk prediction model for diabetic peripheral neuropathic pain in type 2 diabetes: A machine learning and statistical approach.

OBJECTIVE: To identify independent risk factors for diabetic peripheral neuropathic pain (DPNP), construct a nomogram prediction model, and quantify t...

From algorithms to action: Risk stratification for arteriovenous fistula failure in hemodialysis.

BACKGROUND: Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis, yet primary failure and early dysfunction remain common, th...

A sequential deep learning model for predicting people with obesity in adults aged 18-64 using physical fitness variables.

BACKGROUND/OBJECTIVES: Health of people with obesity is a global concern. We developed an explainable sequential deep learning model using nationally ...

Targeting autophagy in Duchenne muscular dystrophy: mechanistic insights and emerging therapeutic strategies.

Duchenne muscular dystrophy (DMD) is a severe X-linked myopathy characterised by progressive skeletal and cardiac muscle degeneration, loss of ambulat...

Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy.

OBJECTIVES: Based on ultrasound technology and clinical indicators, this study intends to develop multiple risk prediction models for diabetic periphe...

Fuzzy logic for preanesthetic risk assessment in cataract surgery patients.

We aimed to build a fuzzy logic preanaesthetic risk score tailored to cataract surgery. By fusing systemic comorbidities with key patient attributes i...

Developing and validating a clinically actionable prediction tool for Parkinson disease using explainable machine learning with multidimensional national data: A cross-sectional study.

Parkinson disease (PD) is a rapidly growing neurodegenerative disorder that presents a significant public health challenge in aging societies, particu...

Advancing diagnostic equity through artificial intelligence chest radiograph screening for osteoporosis in Asian populations.

Early identification of abnormal bone mineral density (BMD) through opportunistic screening is critical for preventing osteoporotic fractures. We vali...

Behavioural, cognitive, and computational risk factors for type 2 diabetes: A systematic review.

BACKGROUND: Type 2 diabetes mellitus represents a global public health challenge, with rising prevalence driven by complex interactions between lifest...

Graph-Enhanced Multi-Task Learning for Type 2 Diabetes Comorbidity Risk Prediction.

Early prediction of Type 2 diabetes mellitus (T2DM) complications holds significant clinical importance for improving patient outcomes and reducing he...

The relationship between sarcopenia and all-cause and cardiovascular mortality risk among middle-aged and older adults across stages 0-3 of cardiovascular-kidney-metabolic syndrome: evidence from NHANES and CHARLS.

BACKGROUND: Sarcopenia has been proved to be associated with cardiovascular diseases, chronic kidney disease, and metabolic disorders, but the relatio...