[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122289-en":3,"doc-seo-122289-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122289,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",7,"Healthcare","Development and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients","Objective: develop a risk prediction model for potentially inappropriate medications (PIM) in elderly stroke patients using multiple machine-learning algorithms to support clinical decision-making and rational medication use. Methods: 1,252 discharged patients (Jan 2023–Dec 2024) were modeled after variable selection with LASSO, trained and internally validated 7:3, and externally validated with 240 patients (Jan–Feb 2025). PIM was defined by the 2023 AGS Beers Criteria. Results: 53.91% had PIM; elastic net showed best performance with AUC 0.894 in external validation, well-calibrated curves, and high net benefit across 15%–97% thresholds. Conclusion: the Enet-based model enables accurate, generalizable identification for targeted interventions to reduce PIM risk.","TYPE Original Research PUBLISHED 23 May 2025  \nDOI 10.3389/fphar.2025.1565420  \nOPEN ACCESS  \nEDITED BY  \nDavid Bin-Chia Wu,  \nNational University of Singapore, Singapore  \nREVIEWED BY  \nJinlong Liu,  \nZhejiang University, China Jiachen Liu,  \nWashington University in St. Louis, United States  \n*CORRESPONDENCE  \nManqin Yang,  \n [yang9988770121@163.com](yang9988770121@163.com)  \nRECEIVED 23 January 2025  \nACCEPTED 12 May 2025  \nPUBLISHED 23 May 2025  \nCITATION  \nYang X, Ye Q, Zhang M, Xu Y and Yang M (2025)  \nDevelopment and validation of a machinelearning model for the risk of potentially inappropriate medications in elderly stroke patients.  \nFront. Pharmacol. 16:1565420 .  \ndoi: 10.3389/fphar.2025.1565420  \nCOPYRIGHT  \n© 2025 Yang, Ye, Zhang, Xu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDevelopment and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients  \nXiaodan Yang, Qianqian Ye, Mengxiang Zhang, Yuewei Xu and Manqin Yang*  \nDepartment of Pharmacy, The second Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China  \nObjective: To construct a risk prediction model for potentially inappropriate medications (PIM) in elderly stroke patients based on multiple machine-learning algorithms, providing decision support to identify high-risk patients and ensure rational clinical medication use.  \nMethods: A total of 1,252 discharged stroke patients from a tertiary hospital in Anhui Province, China, were included from January 2023 to December 2024 . PIM was assessed using the American Geriatrics Society 2023 Updated Beers Criteria® . Univariate analysis identified factors potentially associated with PIM, and the least absolute shrinkage and selection operator regression analysis was applied to select variables. The dataset was randomly split into training and internal validations sets in a 7:3 ratio. Additionally, a dataset independent of the training set in terms of time was selected, consisting of 240 stroke patients diagnosed at the same hospital from January to February 2025, to serve as an external validation cohort. Four machine-learning models, Random Forest, Elastic Net (Enet), Support Vector Machine Classifier, and Extreme Gradient Boosting were built using the meaningful variables identified after selection. The evaluation of machine-learning models was carried out through the discrimination, calibration, and clinical utility. SHapley Additive exPlanation (SHAP) values were utilized to rank the importance of features and to interpret the best-performing model.  \nResults: Among 1,252 patients, 675 (53 . 91%) had PIM, with 107 types and 1,140 occurrences of PIM. Both in internal and external validation cohort, Enet performed the best. The area under the curve (AUC) of Receiver Operating Characteristic (ROC) curve of Enet in external validation set was 0.894 (0.854, 0. 933) . The model’s calibration curve closely followed the ideal curve, and the clinical decision curve showed high net benefit within a threshold probability range of 15%–97% . The results indicate that the Enet prediction model exhibits good accuracy and generalizability, offering a basis for guiding clinical treatment.  \nFrontiers in Pharmacology 01 [frontiersin.org](frontiersin.org)  \nConclusion: The PIM risk prediction model developed using machine-learning can effectively identify PIM, aiding in the implementation of targeted interventions to prevent and reduce the risk of PIM in elderly stroke patients.  \nKEYWORDS  \nstroke, potentially inappropriate medi","cbCaikdntWb5nFax","https://ap.wps.com/l/cbCaikdntWb5nFax","pdf",1978014,1,17,"English","en",105,"# Introduction\n# Methods\n## Data and cohorts\n## Variable selection and model building\n## Evaluation and interpretability\n# Results\n## PIM prevalence\n## Model performance\n# Conclusion","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To construct and validate a machine-learning risk prediction model for potentially inappropriate medications (PIM) in elderly stroke patients to support clinical decision-making.\"},{\"question\":\"How were the training and validation cohorts selected?\",\"answer\":\"A total of 1,252 discharged stroke patients from a tertiary hospital in Anhui were used for training and internal validation (7:3). An external validation cohort of 240 patients from the same hospital during Jan–Feb 2025 was used separately.\"},{\"question\":\"Which machine-learning model performed best and how was it evaluated?\",\"answer\":\"Elastic Net (Enet) performed best. Models were assessed using discrimination (AUC/ROC), calibration, and clinical utility via decision curve analysis, with SHAP used to interpret feature importance.\"}]","Development and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients | PDF",1785809843,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"development-and-validation-of-a-machine-learning-model-for-the-risk-of-potentially-inappropriate-medications-in-elderly-stroke-patients","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/development-and-validation-of-a-machine-learning-model-for-the-risk-of-potentially-inappropriate-medications-in-elderly-stroke-patients/122289/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To construct and validate a machine-learning risk prediction model for potentially inappropriate medications (PIM) in elderly stroke patients to support clinical decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the training and validation cohorts selected?",{"text":80,"@type":76},"A total of 1,252 discharged stroke patients from a tertiary hospital in Anhui were used for training and internal validation (7:3). An external validation cohort of 240 patients from the same hospital during Jan–Feb 2025 was used separately.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning model performed best and how was it evaluated?",{"text":84,"@type":76},"Elastic Net (Enet) performed best. 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