Development and Validation of a Machine Learning Model for Predicting Medication Adherence Among Home-Dwelling Elderly Patients: A Retrospective Cross-Sectional Study.
To develop an interpretable machine learning prediction model that fills the above research gap to predict the medication adherence of elderly patients with chronic diseases in China. Methods: From January to December 2024, data were collected from chronic disease patients aged 60 years and older re...
| Publicado en: | Patient Preference & Adherence Vol. 20; pp. 1 - 18 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Dove Medical Press Ltd
May2026
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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=ccm&AN=194805474&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194805474 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1177889X B06U jtl: Patient Preference & Adherence issn: 1177889X maglogo: N pubinfo: dt: May2026 vid: 20 pid: 45064 pub: Dove Medical Press Ltd place: Auckland, <Blank> artinfo: ui: 194805474 194805474 194805474 10.2147/PPA.S611334 194805474 ppf: 1 ppct: 17 formats: tig: atl: Development and Validation of a Machine Learning Model for Predicting Medication Adherence Among Home-Dwelling Elderly Patients: A Retrospective Cross-Sectional Study. aug: au: Zhang, Yujie Han, Yongli Yin, Xuemei Tian, Yue Wu, Mingfen sug: subj: Machine Learning Prediction Models Evaluation Medication Compliance Evaluation Community Living In Old Age Chronic Disease Drug Therapy Human Retrospective Design Record Review Nonexperimental Studies Comparative Studies Cross Sectional Studies Middle Age Aged Aged, 80 and Over Interviews Pharmacists Comorbidity Self-Efficacy Support, Social Health Literacy Multiple Logistic Regression Machine Learning Algorithms Boosting Machine Learning Algorithms Confidence Intervals China Questionnaires Female Male Marital Status Educational Status Employment Status Income Disease Duration Support Vector Machine Decision Trees Scales Descriptive Statistics Data Analysis Software Mann-Whitney U Test Chi Square Test Fisher's Exact Test Funding Source Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: To develop an interpretable machine learning prediction model that fills the above research gap to predict the medication adherence of elderly patients with chronic diseases in China. Methods: From January to December 2024, data were collected from chronic disease patients aged 60 years and older receiving home-based medication therapy through face-to-face interviews conducted by pharmacists. Variables included demographic information, comorbidities, chronic diseases and medications information, medication adherence, self-efficacy in rational drug use, medication beliefs, social support, and medication literacy. The dataset was randomly divided into a training set and a test set at a 7:3 ratio. Multivariate logistic regression analysis was performed on all data, and predictors were selected from the training set via the Least Absolute Shrinkage and Selection Operator (LASSO). Six machine learning algorithms were applied in R software to develop predictive models using the training set, and their performance was compared on the test set. The Shapley Additive Explanations (SHAP) approach was used to interpret the optimal model. Results: A total of 1722 patients were included in the statistical analysis. The gradient boosting machine (GBM) exhibited the best predictive performance among the six models (AUC = 0.811, 95% CI 0.774– 0.840), with its core predictors being self-efficacy in rational drug use, medication practice, concern beliefs, and availability of social support. Through SHAP analysis, the interpretability of the model was significantly enhanced, providing a clear decision-making basis for clinicians. Conclusion: We constructed a prediction model for home medication adherence in elderly patients with chronic diseases, which incorporates important social and psychological factors affecting patients' adherence and provides robust evidence for developing targeted interventions. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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