AIMC Topic: Risk Assessment

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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 ...

Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning.

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
BACKGROUND: Heart failure (HF) is a major driver of global morbidity and mortality. Early identification of patients at risk remains challenging due to complex, multivariate clinical relationships. Machine learning (ML) methods offer promise for more...

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...

Artificial intelligence in forensic mental health: A review of applications and implications.

Journal of forensic and legal medicine
This narrative review explores the transformative role of artificial intelligence (AI) in forensic mental health, focusing on its applications, benefits, limitations, and ethical considerations. AI's capabilities, particularly in areas such as risk a...

A high-resolution GIS and machine learning approach for targeted disease management and localized risk assessment in an urban setup: A case study from Bhopal, Central India.

Acta tropica
Predicting dengue distribution based on environmental factors is crucial for effective vector control and management as environmental factors like temperature, demographics, and artificial changes such as roads and buildings significantly influence d...

Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure.

Ecotoxicology and environmental safety
Hyperuricemia is a global health concern, with environmental chemicals as risk factors. This study used data of multiple environmental chemical exposures from the 2011-2012 cycle of the National Health and Nutrition Examination Survey (NHANES) to dev...

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...

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...

Risk prediction models for patients with recurrent diabetic foot ulcers: A systematic review.

Public health
OBJECTIVES: To systematically review published studies on risk prediction models for patients with recurrent diabetic foot ulcers.

Optimizing clinical risk stratification of localized prostate cancer.

Current opinion in urology
PURPOSE OF REVIEW: To review the current risk and prognostic stratification systems in localised prostate cancer. To explore some of the most promising adjuncts to clinical models and what the evidence has shown regarding their value.