[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118461-en":3,"doc-seo-118461-105":30,"detail-sidebar-cat-0-en-105":83},{"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},118461,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Prediction of 14-day Hospitalization Risk in Chronic Heart Failure Patients - using Interpretable Machine Learning Methods","Purpose examines whether acute hospitalization risk in chronic heart failure (CHF) patients can be predicted from biweekly pulse, blood pressure, and weight measurements, with a focus on highly interpretable machine learning due to limited uptake of complex models in clinical practice. Methods train three algorithm types—logistic regression, Random Forest, and RuleFit—using 11,575 measurements from 122 patients, evaluated via fivefold cross-validation using f-measure, ROC-AUC, sensitivity, and specificity. Results identify logistic regression with lasso as best, and interpretability supports clinical suitability despite modest overall performance.","Aalborg Universitet  \nPrediction of 14-day Hospitalization Risk in Chronic Heart Failure Patients, using Interpretable Machine Learning Methods  \nXylander, Alexander Arndt Pasgaard; Cichosz, Simon Lebech; Jensen, Morten Hasselstrøm; Hejlesen, Ole; Witt Udsen, Flemming  \nPublished in:  \nHealth and Technology  \nDOI (link to publication from Publisher):  \n10.1007/s12553-025-00957-9  \nCreative Commons License  \nCC BY 4.0  \nPublication date: 2025  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nXylander, A. A. P. , Cichosz, S. L. , Jensen, M. H. , Hejlesen, O. , & Witt Udsen, F. (2025) . Prediction of 14-day Hospitalization Risk in Chronic Heart Failure Patients, using Interpretable Machine Learning Methods. Health and Technology, 15(3), 515-522 . Article e031670 . [https://doi.org/10.1007/s12553-025-00957-9](https://doi.org/10.1007/s12553-025-00957-9)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from [vbn.aau.dk](vbn.aau.dk) on: August 02, 2026  \nHealth and Technology (2025) 15:515–522  \n[https://doi.org/10.1007/s12553-025-00957-9](https://doi.org/10.1007/s12553-025-00957-9)  \nPrediction of 14‑day hospitalization risk in chronic heart failure patients, using interpretable machine learning methods  \nAlexander Arndt Pasgaard Xylander1 · Simon Lebech Cichosz1 · Morten Hasselstrøm Jensen1 · Ole Hejlesen1 · Flemming Witt Udsen1,2  \nReceived: 10 October 2023 / Accepted: 6 March 2025 / Published online: 13 March 2025 © The Author(s) 2025  \nAbstract  \nPurpose We wished to investigate whether the risk of acute hospitalizations of chronic heart failure (CHF) patients, could be predicted from biweekly measurements of pulse, blood pressure and weight. We emphasized machine learning models with a high degree of interpretability, due to low adaptation of complex machine learning models in clinical practice. Methods Using 11,575 measurements of pulse, blood pressure and weight belonging to 122 patients, we trained three types of machine learning algorithms, logistic regression, Random Forest and “RuleFit” to predict nonelective hospitalization within the next 14 days. We used a fivefold cross validation framework to estimate performance metrics, including f-measure,“Receiver Operating Characteristic—Area Under the Curve”(ROC-AUC), sensitivity and specificity.  \nResults A simple interpretable machine learning algorithm, logistic regression with least absolute shrinkage and selection operator (lasso), performed the best. The regression based on simple features performed with a ROC-AUC of 0.622 (sensitivity = 0.185, specificity = 0.93), while the regression based on a more complex feature set performed with a ROC-AUC of 0.657 (sensitivity = 0.212, specificity = 0.921) .  \nConclusion In our study simple interpretable methods, outperformed more complex black box machine learning methods in predicting hospitalization of heart failure patients. This suggests that interpretable methods are appropriate in this context. However, the strength of results are slightly limited by the overall modest performance of the models and the small sample size.","cbCairN3XVcX9lDr","https://ap.wps.com/l/cbCairN3XVcX9lDr","pdf",619649,1,9,"English","en",105,"# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n## Keywords","[{\"question\":\"What are the study’s main limitations and implication?\",\"answer\":\"Performance is modest and the sample size is small, but the findings suggest interpretable methods are appropriate for predicting hospitalization in this clinical context.\"}]","Prediction of 14-day Hospitalization Risk in Chronic Heart Failure Patients - using Interpretable Machine Learning Methods | PDF",1785683729,23,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"prediction-of-14-day-hospitalization-risk-in-chronic-heart-failure-patients-using-interpretable-machine-learning-methods","",{"@graph":36,"@context":77},[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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-14-day-hospitalization-risk-in-chronic-heart-failure-patients-using-interpretable-machine-learning-methods/118461/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What are the study’s main limitations and implication?","Question",{"text":75,"@type":76},"Performance is modest and the sample size is small, but the findings suggest interpretable methods are appropriate for predicting hospitalization in this clinical context.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]