This study presents a machine learning-driven model predicting all-cause mortality two years in advance using administrative health data focused on diabetic patients. Integrating hospitalization records, emergency department data, demographics, and c...
BMC medical informatics and decision making
Oct 13, 2025
BACKGROUND: Clinical utilization of machine learning is hampered by the lack of interpretability inherent in most non-linear black box modeling approaches, reducing trust among clinicians and regulators. Advanced large language models offer a potenti...
Environmental geochemistry and health
Oct 13, 2025
This study aims to evaluate key parameters of groundwater quality and associated health risks in three coastal aquifers of Cox's Bazar, Bangladesh, with a focus on manganese contamination and geochemical processes. A total of 288 groundwater samples ...
This study aims to enhance individual hypertension risk prediction in Indonesia using machine learning (ML) models. The research investigates the predictive accuracy of models with and without incorporating personal hypertension history, seeking to u...
BACKGROUND: Cardiovascular disease remains the predominant cause of morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). Traditional risk models are limited in predictive accuracy. Pericoronary adipose tissue (PCAT), a novel i...
BMC medical informatics and decision making
Oct 10, 2025
OBJECTIVE: To develop and evaluate machine learning models combined with survival analysis for predicting 7-, 14-, and 28-day mortality in critically ill children with acute kidney injury (AKI), identifying key predictors to guide risk stratification...
OBJECTIVE: Primary palmar hyperhidrosis (PPH), characterised by excessive palm sweating, significantly impacts patients' physiology, psychology, self-esteem, work, life and social interactions. The incidence of depression is higher among PPH patients...
OBJECTIVE: This review synthesizes atmospheric chemistry, toxicology, and environmental justice to assess public health risks from ultrafine particles (UFPs, <100 nm) in Wildland-Urban Interface (WUI) fire smoke. It explores UFP emission sources, tra...
BACKGROUND: Previous studies have demonstrated that the triglyceride-glucose (TyG) index in combination with the estimated glucose disposal rate (eGDR) could predict mortality risks in the normal population. Our studies have focused on their additive...
PURPOSE OF REVIEW: This review explores the role of artificial intelligence (AI) in visceral adipose tissue (VAT) and ectopic fat imaging. It aims to evaluate how AI may be used to enhance the efficiency and accuracy of cardiovascular disease (CVD) r...
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