AIMC Topic: Risk Assessment

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Enhancing fall risk assessment: instrumenting vision with deep learning during walks.

Journal of neuroengineering and rehabilitation
BACKGROUND: Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking pro...

Black-white differences in chronic stress exposures to predict preterm birth: interpretable, race/ethnicity-specific machine learning model.

BMC pregnancy and childbirth
BACKGROUND: Differential exposure to chronic stressors by race/ethnicity may help explain Black-White inequalities in rates of preterm birth. However, researchers have not investigated the cumulative, interactive, and population-specific nature of ch...

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers.

Scientific reports
Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible ...

Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer.

European urology focus
BACKGROUND: The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual's cancer risk...

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ.

The Science of the total environment
Short-term exposure to ground-level ozone (O) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O forecasting to mitigate these risks, focusing on ...

Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach.

Cardiovascular diabetology
BACKGROUND: Various surrogate markers of insulin resistance have been developed, capable of predicting coronary artery disease (CAD) without the need to detect serum insulin. For accurate prediction, they depend only on glucose and lipid profiles, as...

Development and validation of machine learning models to predict perioperative transfusion risk for hip fractures in the elderly.

Annals of medicine
BACKGROUND: Patients with hip fractures frequently need to receive perioperative transfusions of concentrated red blood cells due to preoperative anemia or surgical blood loss. However, the use of perioperative blood products increases the risk of ad...

ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data.

Environmental science. Processes & impacts
Due to the lack of experimental toxicity data for environmental chemicals, there arises a need to fill data gaps by approaches. One of the most commonly used approaches for toxicity assessment of small datasets is the Quantitative Structure-Activit...

Systematic review of machine learning models in predicting the risk of bleed/grade of esophageal varices in patients with liver cirrhosis: A comprehensive methodological analysis.

Journal of gastroenterology and hepatology
Esophageal varices (EV) in liver cirrhosis carry high mortality risks. Traditional endoscopy, which is costly and subjective, prompts a shift towards machine learning (ML). This review critically evaluates ML applications in predicting bleeding risks...