Obesity

Risk Factors for Postoperative Delirium in Nonintensive Care Unit Patients: Machine Learning Approach.

Postoperative delirium is a frequent and serious complication lacking effective prediction tools for general ward patients. This study aimed to identify postoperative delirium risk factors in non-ICU patients using machine learning. A retrospective analysis of 85,884 surgical patients (2017-2022) from a tertiary hospital was conducted. Postoperative delirium, diagnosed from nursing records, was pr...

Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.

BACKGROUND: Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in routine care, particularly in home health care (HHC), where clinical decisions are made under time constraints and cognitive status may be incompletely documented. Federally mandated HHC assessments, such as the Outcome and Assessment Information Set (OASIS), capture health and functional status but ma...

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation.

BACKGROUND: Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, base...

Advancements in psoriasis classification using custom transfer learning algorithms.

Psoriasis is a skin disorder which mainly occurs as a rash, scaly areas and an itchy skin. The symptoms usually occur on the chest, elbows, and the sc...

Machine learning for the diagnosis of fibromyalgia based on magnetic resonance imaging.

The clinical diagnosis of fibromyalgia (FM), a syndrome characterized by generalized pain, is challenging due to its unknown etiology and frequent com...

National data meets AI: Machine learning for predicting overweight/obesity among ever-married Bangladeshi women.

Overweight/obesity has become a critical global health issue, as these conditions are strongly associated with elevated risk of diabetes, stroke, card...

Predicting Treatment Outcomes in Patients With Psoriatic Arthritis or Axial Spondyloarthritis: An Artificial Intelligence-Driven Approach.

OBJECTIVE: To develop machine learning (ML) models to predict the probability at baseline of achieving low disease activity (LDA) and high health-rela...

A 192-Channel 1D CNN-Based Neural Feature Extractor in 65nm CMOS for Brain-Machine Interfaces.

We present a 192-channel 1D convolutional neural network (1D CNN) based neural feature extractor for Brain-Machine Interfaces (BMI) that achieves stat...

AcuKG: a comprehensive knowledge graph for medical acupuncture.

BACKGROUND: Acupuncture, a key modality in traditional Chinese medicine, is gaining global recognition as a complementary therapy and a subject of inc...

Artificial Intelligence in Nutrigenomics: A Critical Review on Functional Food Insights and Personalized Nutrition Pathways.

BACKGROUND: This review critically evaluates the applications of artificial intelligence in nutrigenomics, focusing on its role in interpreting functi...

Establishment and validation of a machine learning model to stratify malnutrition risk in hospitalized older patients with chronic heart failure.

BACKGROUND AND OBJECTIVES: Malnutrition among older hospitalized adults with chronic heart failure (CHF) is associated with adverse clinical outcomes,...

Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis.

Purpose To evaluate the predictive value of myosteatosis as an opportunistic finding in coronary artery calcium (CAC) CT scans for clinically diagnose...

Analysis of Clinical Improvement at 90 Days After Varicose Vein Surgery Using Machine Learning.

Outcomes after intervention for chronic venous insufficiency (CVI) is difficult to predict. This study aimed to develop machine learning (ML) models t...

Prediction of mortality, bleeding, and ischaemic events in patients with cancer and acute coronary syndrome: a model development and validation study.

BACKGROUND: Accurate assessment of mortality, bleeding, and atherothrombotic risk in patients with cancer and acute coronary syndrome could inform nov...

From non-specific biomarker to targeted action: transdiagnostic and sex-specific drivers of high-CRP status in severe mental illness across the FondaMental Advanced Centers of Expertise (FACE) cohorts.

BACKGROUND AND OBJECTIVES: Low-grade systemic inflammation contributes to the pathophysiology of severe mental illness (SMI) in a substantial subset o...

Vigorous intermittent lifestyle physical activity (VILPA) and mortality risk among US adults: a wearables-based national cohort study.

Gut microbiome signatures associated with depression and obesity.

UNLABELLED: Depression and obesity are highly comorbid and likely involve common risk factors and pathophysiological mechanisms, which could crosslink...

Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution.

PURPOSE: Radiation-free tools, such as scoliometers, ultrasound, and Moiré topography, have been explored for monitoring Adolescent Idiopathic Scolios...

Machine learning prediction of food addiction in university students using demographic, anthropometric and personality traits.

People's eating habits are influenced by psychological, social, cultural, and behavioral factors. Research shows that certain personality types expose...

Deep learning enhanced ALPS reveals genetic and environmental factors of brain glymphatic function.

BACKGROUND: The glymphatic system plays a critical role in brain waste clearance and health. Diffusion tensor imaging along the perivascular space (DT...