Artificial Intelligence driven prediction of multiple outcomes in older adults with coronary heart disease.

Background and Objectives Frail older adults with coronary heart disease (CHD) face significantly elevated risks of adverse clinical outcomes, including mortality, prolonged hospitalizations, and frequent readmissions. Conventional risk stratification tools, inadequately account for frailty and mult...

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Detalles Bibliográficos
Publicado en:	Gerontologist Vol. 66; no. 4; pp. 1 - 18
Autores principales:	Tesha, Innocent, Qi, Meng, JiaSi, Wang, Xizhe, Zhao, Kombo, Ahmed, Njoka, Irene, Abdelilah, Maliki, Janabi, Mohammed, Xinyu, Liu
Formato:	Artículo
Publicado:	Oxford University Press / USA Apr2026
Materias:	Mortality Elder care Artificial intelligence Retrospective studies Hospital care of older people Psychosocial factors Old age Coronary heart disease risk factors Risk assessment Random forest algorithms Boosting algorithms Prediction models Receiver operating characteristic curves T-test (Statistics) Frail elderly Patient readmissions Clinical decision support systems Multiple regression analysis Descriptive statistics Mann Whitney U Test Chi-squared test Longitudinal method Medical records Acquisition of data Electronic health records Length of stay in hospitals Data analysis software Confidence intervals Factor analysis Comorbidity Biomarkers Sensitivity & specificity (Statistics)
Acceso en línea:	Ver este registro en EBSCOhost