AIMC Topic: Risk Factors

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Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Journal of pharmaceutical and biomedical analysis
Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop a predictive model for occupational As exposure and hypertension using metabolomics and machine lea...

State-of-the-art analysis of electrocardiogram findings in sudden cardiac death.

Heart (British Cardiac Society)
Sudden cardiac death (SCD) is a significant public health issue, and efforts to prevent it have involved the analysis of various modalities, including echocardiography, cardiac CT, cardiac MRI, genetic testing and ECG. The ECG, invented >100 years ag...

Machine learning approaches for predicting prolonged hospital length of stay after lumbar fusion surgery in patients aged 75 years and older: a retrospective cohort study based on comprehensive geriatric assessment.

Neurosurgical focus
OBJECTIVE: Postoperative recovery following lumbar fusion surgery in patients aged 75 years and older often requires a prolonged length of stay (PLOS) in the hospital. Accurately predicting the risk of PLOS and assessing its risk factors for preopera...

Phenotype-driven risk stratification of cerebral aneurysms using Shapley Additive Explanations-based supervised clustering: a novel approach to rupture prediction.

Neurosurgical focus
OBJECTIVE: The aim of this study was to address the limitations of traditional aneurysm risk scoring systems and computational fluid dynamics (CFD) analyses by applying a supervised clustering framework to identify distinct aneurysm phenotypes and im...

Fundus Refraction Offset as a Personalized Biomarker for 12-Year Risk of Retinal Detachment.

Investigative ophthalmology & visual science
PURPOSE: The purpose of this study was to investigate the potential of a novel anatomical metric of ametropia-fundus refraction offset (FRO)-in stratifying the risk of retinal detachment (RD) or breaks, beyond the influence of risk factors including ...

Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.

Clinical transplantation
INTRODUCTION: Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research.

Optimization enabled ensemble based deep learning model for elderly falling risk prediction.

Computer methods in biomechanics and biomedical engineering
Predicting fall risk in the elderly is crucial for enhancing safety and well-being. Aging and chronic diseases often impair balance, increasing fall risk. This study aims to develop an advanced fall risk prediction model using an optimized deep learn...

Volatomics for Diagnosis and Risk Stratification of MASLD: A Proof-Of-Concept Study.

Alimentary pharmacology & therapeutics
BACKGROUND AND AIMS: Human breath contains numerous volatile organic compounds (VOCs) produced by physiological and metabolic processes or perturbed in pathological states. Electronic nose (eNose) technology has been extensively validated as a non-in...

Machine learning applications related to suicide in military and Veterans: A scoping literature review.

Journal of biomedical informatics
OBJECTIVE: Suicide remains one of the main preventable causes of death among service members and veterans. Early detection and accurate prediction are essential components of effective suicide prevention strategies. Machine learning techniques have b...

A lightweight graph neural network to predict long-term mortality in coronary artery disease patients: an interpretable causality-aware approach.

Journal of biomedical informatics
BACKGROUND: Coronary artery disease (CAD) causes substantial death toll in the United States and worldwide. While traditional methods for CAD mortality prediction are based on established risk factors, they have significant limitations in accuracy, a...