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...
| Publicado en: | Gerontologist Vol. 66; no. 4; pp. 1 - 18 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
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Oxford University Press / USA
Apr2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192902136&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192902136 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00169013 GET jtl: Gerontologist issn: 00169013 maglogo: N pubinfo: dt: Apr2026 vid: 66 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 192902136 10.1093/geront/gnag017 ppf: 1 ppct: 17 formats: tig: atl: Artificial Intelligence driven prediction of multiple outcomes in older adults with coronary heart disease. aug: au: Tesha, Innocent Qi, Meng JiaSi, Wang Xizhe, Zhao Kombo, Ahmed Njoka, Irene Abdelilah, Maliki Janabi, Mohammed Xinyu, Liu affil: Department of Geriatrics Medicine, First Affiliated Hospital, Jinzhou Medical University, Liaoning, ChinaDepartment of Internal Medicine, Muhimbili National Hospital, Dar-es-salaam, Tanzania Department of Geriatrics Medicine, First Affiliated Hospital, Jinzhou Medical University, Liaoning, China Department of Medical Sciences, Jinzhou Medical University, Liaoning, China Department of Internal Medicine, Muhimbili National Hospital, Dar-es-salaam, Tanzania su: 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) sug: subj: Mortality Elder care Artificial intelligence Retrospective studies Hospital care of older people Psychosocial factors Old age Continuing Care Retirement Communities 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) keyword: Frailty Multimorbidity Personalized care Frailty Multimorbidity Personalized care ab: 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 multimorbidity, limiting their effectiveness in geriatric care. To address this gap, we developed and validated the first machine learning (ML) model that integrates frailty into a multi-outcome risk assessment framework, thereby enhancing clinical decision-making in geriatric cardiology. Research Design and Methods Utilizing electronic health records from hospitalized frail CHD patients, we developed a multinomial prediction model employing advanced ML techniques, including principal component analysis, gradient boosting, and random forest. The model incorporates explainable artificial intelligence (AI) features to enhance interpretability and a clinical applicability, prioritizing key predictors such as biomarkers and comorbidities. Results The ML model demonstrated superior predictive performance with receiver operating characteristic curve analysis (area under the curve 0.94, 95% CI: 0.88–1.00) for mortality, 0.72 (95% CI: 0.55–0.87) readmission, and 0.68 (95% CI: 0.57–0.77) prolonged hospital stay, enabling earlier risk identification and personalized intervention strategies. Discussion and Implications This AI-driven approach represents a significant advancement in geriatric cardiology designed for integration into hospital dashboards, providing real-time patient-centered decision support, optimization of clinical workflow and resource allocation. By advancing digital health solutions and AI driven precision medicine, this model sets a new standard for digital health innovations in aging care. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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