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...
| Publicado en: | Age & Ageing Vol. 54; no. 6; pp. 1 - 10 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Jun2025
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| 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=186318531&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 186318531 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00020729 AGA jtl: Age & Ageing issn: 00020729 maglogo: N pubinfo: dt: Jun2025 vid: 54 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 186318531 10.1093/ageing/afaf156 ppf: 1 ppct: 9 formats: tig: atl: Potentially inappropriate polypharmacy is an important predictor of 30-day emergency hospitalisation in older adults: a machine learning feature validation study. aug: au: Olender, Robert T Roy, Sandipan Nishtala, Prasad S affil: Department of Pharmacy and Pharmacology, University of Bath, Claverton Down, Bath BA2 7AY, UK Department of Mathematical Sciences, University of Bath, Bath, UK Department of Pharmacy and Pharmacology & Centre for Therapeutic Innovation, University of Bath, Bath, UK su: 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 sug: subj: Smoking Artificial intelligence Hospital care of older people Sociodemographic factors Alcoholism United Kingdom Uk Biobank Ltd. 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 keyword: artificial intelligence decision tree hospitalisation machine learning older people predictive modelling artificial intelligence decision tree hospitalisation machine learning older people predictive modelling ab: 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 identified by these models must be validated in diverse populations. Methods In this cohort study, 86 870 community-dwelling older adults ≥65 years from the UK Biobank database were used to train and test three ML models to predict 30-day emergency hospitalisation. The three ML models, Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR), utilised all extracted variables, consisting of demographic and geriatric syndromes, comorbidities, and the Drug Burden Index (DBI), a measure of potentially inappropriate polypharmacy, which quantifies exposure to medications with anticholinergic and sedative properties. 30-day emergency hospitalisation was defined as any hospitalisation related to any clinical event within 30 days of the index date. The model performance metrics included the area under the receiver operating characteristics curve (AUC-ROC) and the F1 score. Results The AUC-ROC for the RF, XGB and LR models was 0.78, 0.86 and 0.61, respectively, signifying good discriminatory power. The DBI, mobility, fractures, falls, hazardous alcohol drinking and smoking were validated as important variables in predicting 30-day emergency hospitalisation. Conclusions This study validated important risk factors for predicting 30-day emergency hospitalisation. The validation of important risk factors will inform the development of future ML studies in geriatrics. Future research should prioritise the development of targeted interventions to address the risk factors validated in this study, ultimately improving patient outcomes and alleviating healthcare burdens. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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