Potentially inappropriate polypharmacy is an important predictor of 30-day emergency hospitalisation in older adults: a machine learning feature validation study.

Background Machine learning (ML) models in healthcare are crucial for predicting clinical outcomes, and their effectiveness can be significantly enhanced through improvements in accuracy, generalisability, and interpretability. To achieve widespread adoption in clinical practice, risk factors identi...

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Bibliographic Details
Published in:	Age & Ageing Vol. 54; no. 6; pp. 1 - 10
Main Authors:	Olender, Robert T, Roy, Sandipan, Nishtala, Prasad S
Format:	Article
Published:	Oxford University Press / USA Jun2025
Subjects:	United Kingdom Uk Biobank Ltd. Smoking Artificial intelligence Hospital care of older people Sociodemographic factors Alcoholism Inappropriate prescribing (Medicine) Risk assessment Random forest algorithms Parasympathomimetic agents Prediction models Research funding Receiver operating characteristic curves Logistic regression analysis Frail elderly Polypharmacy Hospital emergency services Descriptive statistics Longitudinal method Research methodology Machine learning Length of stay in hospitals Confidence intervals Comorbidity Physical mobility Accidental falls
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