[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120246-en":3,"doc-seo-120246-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},120246,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Established machine learning models to predict readmission for elderly patients with ischemic heart disease - Research","Background: Clinical features linked to 30-day or 1-year readmission in elderly patients with ischemic heart disease (IHD) and their predictive value remain insufficiently studied. Aims: Develop 30-day and 1-year readmission prediction models using machine-learning features routinely captured at discharge, and assess prognostic impact. Methods: Eight algorithms were compared using AUROC and AUPRC, with SHAP used to interpret feature contributions. Results: 6687 patients were included; categorical boosting performed best for both horizons. Conclusions: High-risk elderly IHD patients can be identified using discharge-collected data.","􀂄 ORIGINAL ARTICLE  \nEstablished machine learning models to predict readmission for elderly patients with ischemic heart disease  \nXuewu Song1, Feng Xian2, Changyu Zhu1, Yi Luo1, Yilong Liu1, QingWen3, Rongsheng Tong1  \n1Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China 2Department of Oncology, Nanchong Central Hospital, 2nd Clinical Medical College, North Sichuan Medical College, Nanchong, China 3Department of Cardiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China  \nCorrespondence to:  \nRongsheng Tong, PhD, Department of Pharmacy, Sichuan Provincial People’s Hospital,  \nUniversity of Electronic Science and Technology of China,  \n32 West Second Section,  \nFirst Ring Rd, Qingyang District, Chengdu 610072, Sichuan, China, phone: +86 028 8739 3485, [e-mail: 318004031@qq.com](e-mail: 318004031@qq.com)[ ](e-mail: 318004031@qq.com)Copyright by the Author(s), 2024 DOI: 10.33963/v.phj.101308  \nReceived:  \nDecember 28, 2023  \nAccepted:  \nJune 24, 2024  \nEarly publication date:  \nJune 28, 2024  \nA B S T R A C T  \nBackground: The contribution of clinical features associated with 30-day or 1-year readmission in elderly patients with ischemic heart disease (IHD) and whether these features can be used to predict the readmission risk of patients has not been studied.  \nAims: The study aimed to develop 30-day and 1-year readmission prediction models for elderly IHD patients using combined machine learning features routinely collected at the time of hospital discharge, and to investigate their prognostic impact.  \nMethods: Eight machine learning algorithms were used to develop prediction models. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) were used to assess discrimination. SHapley Additive exPlanations (SHAP) analysis was used to explain the contribution of features.  \nResults: A total of 6687 patients were enrolled. For 30-day readmissions, the categorical boosting (CB) model had the best predictive performance with the highest AUROC (0.72), and the Brier score was 0.23. For 1-year readmissions, the CB model had the best predictive performance with the highest AUROC (0.66), and the Brier score was 0.14. The age-adjusted Charlson comorbidity index, brain natriuretic peptide, heart failure, cholesterol, free thyroxine, thymidine kinase 1, osmotic pressure, and red blood cell distribution width (standard deviation) were the common important features to predict 30-day and 1-year readmissions of elderly IHD patients.  \nConclusions: Elderly IHD patients with high risk of 30-day or 1-year readmission can be identified using machine learning and features collected at the time of discharge.  \nKey words: elderly, explainable, ischemic heart disease, machine learning, readmission  \nINTRODUCTION  \nIschemic heart disease (IHD), a common chronic non-communicable cardiovascular disease (CVD), is one of the major contributors toCVD-related disease burden [1]. Due to poor disease control, many IHD patients return to the hospital after discharge. For elderly IHD patients and their families, frequent readmissions may increase the financial burden and reduce the quality of life [2] . Therefore, identifying high-risk readmission patients and providing individualized therapeutic regimens is the key to reducing the readmission rate of elderly IHD patients.  \nMany features such as age [3], sex [4], length of stay (LOS) [5] , and complications  \n[6] associated with patient readmission have been described. Results of some routine laboratory tests were also associated with rehospitalization [7, 8] . However, it has not been possible to determine the impact of these features on patient readmission rates orto assess the effect of these features on patient clinical outcomes. Moreover, it is not clear whether the elderly IHD patients at high risk of read","cbCaitI7uOWwKWRx","https://ap.wps.com/l/cbCaitI7uOWwKWRx","pdf",2807016,1,9,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Participants and outcome\n## Machine learning models and evaluation","[{\"question\":\"What was the main goal of the study for elderly IHD patients?\",\"answer\":\"To develop prediction models for 30-day and 1-year readmission risk using machine-learning features routinely collected at hospital discharge, and to evaluate their prognostic impact.\"},{\"question\":\"How were the prediction models evaluated in the study?\",\"answer\":\"Model discrimination was assessed using AUROC and AUPRC, while calibration was also reported for the best-performing model.\"},{\"question\":\"Which machine learning model performed best for readmission prediction?\",\"answer\":\"The categorical boosting (CB) model showed the best predictive performance for both 30-day (highest AUROC 0.72) and 1-year (highest AUROC 0.66) readmissions.\"}]","Established machine learning models to predict readmission for elderly patients with ischemic heart disease - Research | PDF",1785728973,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"established-machine-learning-models-to-predict-readmission-for-elderly-patients-with-ischemic-heart-disease-research","",{"@graph":36,"@context":86},[37,54,69],{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/established-machine-learning-models-to-predict-readmission-for-elderly-patients-with-ischemic-heart-disease-research/120246/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What was the main goal of the study for elderly IHD patients?","Question",{"text":76,"@type":77},"To develop prediction models for 30-day and 1-year readmission risk using machine-learning features routinely collected at hospital discharge, and to evaluate their prognostic impact.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the prediction models evaluated in the study?",{"text":81,"@type":77},"Model discrimination was assessed using AUROC and AUPRC, while calibration was also reported for the best-performing model.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performed best for readmission prediction?",{"text":85,"@type":77},"The categorical boosting (CB) model showed the best predictive performance for both 30-day (highest AUROC 0.72) and 1-year (highest AUROC 0.66) readmissions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]