Obesity

Development and external validation of an interpretable machine learning model for predicting prolonged postoperative ICU length of stay in coronary artery bypass grafting patients using MIMIC-IV 3.1 and eICU-CRD 2.0.

BACKGROUND: Prolonged postoperative intensive care unit (ICU) length of stay (LOS) after coronary artery bypass grafting (CABG) drives resource use yet remains difficult to anticipate at the 24-hour ICU evaluation moment when extended monitoring beyond 72 h is first considered. We developed and externally validated an interpretable machine-learning decision-support calculator for this deployment m...

Feasibility of image-based 3D avatar anthropometry assessment of pediatric obesity: a pilot study.

UNLABELLED: The purpose of this study is to evaluate the feasibility and validity of 3D avatar-based anthropometry for assessing anthropometric indicators of overweight and obesity in children based on artificial intelligence-derived 3D body reconstruction. This cross-sectional study included 171 children aged 8-10 years from five primary schools in southern Spain. Due to technical constraints of ...

An interpretable predictive model for depression risk in diabetic patients: A web-based application using NHANES data.

Depression is a common comorbidity in individuals with diabetes and is associated with adverse clinical outcomes. Early identification of high-risk in...

Machine learning-based early kidney stone risk classification in diabetic-hypertensive patients using obesity composite indices.

Kidney stone disease (KSD) is increasingly prevalent among patients with diabetes mellitus and hypertension. Obesity-related metabolic abnormalities m...

Integrative analysis of machine learning and single-cell sequencing identifies IGHG1 as a therapeutic target for epimedium in diabetes-gastric cancer comorbidity.

BACKGROUND: The clinical comorbidity of diabetes mellitus (DM) and gastric cancer (GC) presents a significant healthcare challenge, as these two condi...

Using machine learning to identify individuals at elevated risk of diabetes from low-cost complete blood count data for opportunistic screening.

Type 2 diabetes and prediabetes remain substantially underrecognized, highlighting the need for practical screening approaches based on routinely avai...

Bayesian-optimized machine learning models for classifying metabolic syndrome control among NCD patients in Bangladesh hospitals.

Control of metabolic syndrome (MS) is still a major challenge for patients suffering from Non-communicable diseases (NCDs) in Bangladesh. In this stud...

A conceptual agentic AI architecture for MASLD-associated significant fibrosis in primary care.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent yet often underdiagnosed or undertreated in primary care due to a...

The systems medicine view of semaglutide: from clinical trials to molecular mechanisms.

INTRODUCTION: Semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1RA), is a key therapy in managing type 2 diabetes (T2D), offering signific...

Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning.

BACKGROUND: Machine learning (ML) shows promise in using clinical data to predict chronic diseases. However, its application in PMOP risk assessment u...

Retinal Vessel Traits and the Risk of Stroke: The Canadian Longitudinal Study on Aging.

PURPOSE: Retinal vascular changes could serve as an early, easily visible indicator of cerebrovascular health. Prior research on retinal vessel traits...

Preoperative considerations for nonoperating room anesthesia.

PURPOSE OF THE REVIEW: This review aims to address the unique challenges in nonoperating room anesthesia (NORA) locations, emphasizing the importance ...

Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.

BACKGROUND: Hypertension serves as a prevalent health issue, particularly in South Asia, where it is also a risk factor and comorbidity that affects t...

Precision Nutrition and Chronic Disease: Integrating Genomics, Microbiome, and Digital Health for Personalized Dietary Interventions.

Chronic non-communicable diseases (NCDs), including cardiovascular disease, type 2 diabetes, obesity, and metabolic syndrome, account for more than 70...

Predicting High-Risk Colorectal Polyps in African Americans Using Pre-colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation.

PURPOSE: Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing inter...

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 approach...

Integrating artificial intelligence and machine learning into risk prediction for type 2 diabetes: A model for early identification of high-risk populations.

Family history, body mass index (BMI), and ethnicity are three key, well-established determinants of susceptibility to type 2 diabetes mellitus (T2DM)...

Effects of breast cancer risk factors on the tumor microenvironment: a morphological deep-learning analysis of three prospective cohorts.

Emerging evidence suggests a critical role of the tumor microenvironment (TME) in breast cancer (BC) development and outcomes, yet factors that modify...

Comprehensive behavioral and demographic correlates in myopia risk among Tibetan adolescents: a machine learning analysis using non‑cycloplegic refraction.

Myopia is a global public health concern, especially among children and adolescents. This exploratory study aimed to develop a myopia risk prediction ...

Automated Assessment of Surgical Quality in Distal Gastrectomy: Development of a Novel Computer Vision Model Based on the Critical View of Quality (CVQ).

OBJECTIVE: To evaluate the clinical validity of the critical view of quality (CVQ) as a measure of lymphadenectomy quality in minimally invasive dista...