AIMC Topic: Risk Factors

Clear Filters Showing 1651 to 1660 of 2857 articles

Pre-existing and machine learning-based models for cardiovascular risk prediction.

Scientific reports
Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascul...

Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning.

Circulation. Genomic and precision medicine
BACKGROUND: Genetic testing can determine family screening strategies and has prognostic and diagnostic value in hypertrophic cardiomyopathy (HCM). However, it can also pose a significant psychosocial burden. Conventional scoring systems offer modest...

Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh.

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: Hypertension has become a major public health issue as the prevalence and risk of premature death and disability among adults due to hypertension has increased globally. The main objective is to characterize the risk factors of h...

Sex-Specific Classification of Drug-Induced Torsade de Pointes Susceptibility Using Cardiac Simulations and Machine Learning.

Clinical pharmacology and therapeutics
Torsade de Pointes (TdP), a rare but lethal ventricular arrhythmia, is a toxic side effect of many drugs. To assess TdP risk, safety regulatory guidelines require quantification of hERG channel block in vitro and QT interval prolongation in vivo for ...

A Machine Learning Approach to First Pass Reperfusion in Mechanical Thrombectomy: Prediction and Feature Analysis.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
INTRODUCTION: Novel machine learning (ML) methods are being investigated across medicine for their predictive capabilities while boasting increased adaptability and generalizability. In our study, we compare logistic regression with machine learning ...

Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: The number of deaths from COVID-19 continues to surge worldwide. In particular, if a patient's condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery.

Assessing the Outbreak Risk of Epidemics Using Fuzzy Evidential Reasoning.

Risk analysis : an official publication of the Society for Risk Analysis
Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID-19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk asses...

Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh.

Informatics for health & social care
Childhood stunting is a serious public health concern in Bangladesh. Earlier research used conventional statistical methods to identify the risk factors of stunting, and very little is known about the applications and usefulness of machine learning (...

Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis.

Circulation
BACKGROUND: Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine...

Predicting women with depressive symptoms postpartum with machine learning methods.

Scientific reports
Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's health, many women do not receive adequate care. Preventive interventions are cost-efficient among high...