AIMC Topic: Socioeconomic Factors
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Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis.
Circulation
Apr 13, 2021
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...
Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.
Scientific reports
Mar 26, 2021
Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributio...
Modeling anxiety and fear of COVID-19 using machine learning in a sample of Chinese adults: associations with psychopathology, sociodemographic, and exposure variables.
Anxiety, stress, and coping
Jan 26, 2021
OBJECTIVES: Research during prior virus outbreaks has examined vulnerability factors associated with increased anxiety and fear.
Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis.
BMC cancer
Nov 27, 2020
BACKGROUND: Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). However, this operation is complex, and the peri-operative risk is high, making patients more likely to be admitted to the intensive care unit (ICU). The...
Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.
Journal of the American Heart Association
Nov 3, 2020
Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community-based interventions have been shown to be both effective and cost-effective in preventing cardiovascul...
Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.
PLoS neglected tropical diseases
Sep 24, 2020
The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were dev...
COVID-19: The role of artificial intelligence in empowering the healthcare sector and enhancing social distancing measures during a pandemic.
South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde
Jun 3, 2020
Indiscriminatory in its spread, COVID-19 has engulfed communities from all social backgrounds throughout the world. While healthcare professionals work tirelessly testing for the virus and caring for patients, they too have become casualties of the p...
Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.
Epilepsia
May 20, 2020
OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics.
Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.
Scientific reports
Mar 24, 2020
Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric histo...
Use of Stratified Cascade Learning to predict hospitalization risk with only socioeconomic factors.
Journal of biomedical informatics
Feb 20, 2020
BACKGROUND AND OBJECTIVE: Published models predicting health related outcomes rely on clinical, claims and social determinants of health (SDH) data. Addressing the challenge of predicting with only SDH we developed a novel framework termed Stratified...
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