[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118008-en":3,"doc-seo-118008-105":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},118008,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Application of machine learning in predicting frailty syndrome in patients with heart failure","Prevention and diagnosis of frailty syndrome in patients with heart failure require innovative systems that enable clinicians to tailor and optimize treatment and care. Traditional approaches combining tests and self-reporting can be time-consuming and costly, and may not fully satisfy clinical needs. Artificial intelligence can analyze physical and psychosocial frailty domains in cardiac patients. This paper reviews machine-learning applications for frailty syndrome, summarizes frailty measurements used in contemporary practice, and provides algorithm recommendations. It also demonstrates an example using the Tilburg Frailty Indicator with psychosocial variables.","Application of machine learning in predicting frailty syndrome in patients with heart failure  \nRemigiusz Szczepanowski1,A,C–F, Izabella Uchmanowicz2,3,A,C–F, Aleksandra H. Pasieczna-Dixit4,A,C–F, Janusz Sobecki1,C, D, Radosław Katarzyniak1,C, D, Grzegorz Kołaczek1,C, D, Wojciech Lorkiewicz1,C, D, Maja Kędras1,C, D, Anant Dixit1,C, D, Jan Biegus3,5,C, D, Marta Wleklik2,3,C, D, Robbert J.J. Gobbens6,7,C, D, Loreena Hill8,C, D, Tiny Jaarsma9,C, D, Amir Hussain10,C, D, Mario Barbagallo11,C, D, Nicola Veronese11,C, D, Francesco C. Morabito12,C, D, Aleksander Kahsin13,C, D  \n1 Department of Computer Science and Systems Engineering, Wroclaw University of Science and Technology, Poland  \n2 Department of Nursing and Obstetrics, Faculty of Health Sciences, Wroclaw Medical University, Poland  \n3 Institute of Heart Diseases, University Hospital, Wrocław, Poland  \n4 Socio-Economic Department, Pomeranian Higher School, Starogard Gdański, Poland  \n5 Institute for Heart Diseases, Wroclaw Medical University, Poland  \n6 Faculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Amsterdam, the Netherlands  \n7 Zonnehuisgroep Amstelland, Amstelveen, the Netherlands  \n8 Department Family Medicine and Population Health, Faculty of Medicine and Health Sciences, University of Antwerp, Belgium  \n9 Tranzo, Tilburg University, the Netherlands  \n10 School of Computing, Edinburgh Napier University, UK  \n11 Geriatric Unit, Department of Internal Medicine and Geriatrics, University of Palermo, Italy  \n12 Mediterranea University of Reggio Calabria (DICEAM), Italy  \n13 Faculty of Medicine, Medical University of Gdansk, Poland  \nA – research concept and design; B – collection and/or assembly of data; C – data analysis and interpretation; D – writing the article; E – critical revision of the article; F – final approval of the article  \nAdvances in Clinical and Experimental Medicine, ISSN 1899–5276 (print), ISSN 2451–2680 (online) Adv Clin Exp Med. 2024;33(3):309–315  \nAddress for correspondence  \nIzabella Uchmanowicz  \nE-mail: [izabella.uchmanowicz@umw.edu.pl](izabella.uchmanowicz@umw.edu.pl)  \nFunding sources  \nThis research was partially funded by the National Science Centre (Poland) under grant No. 2021/41/B/NZ7/01698 .  \nConflict of interest  \nNone declared  \nReceived on August 20, 2023  \nReviewed on January 30, 2024  \nAccepted on February 13, 2024  \nPublished online on March 26, 2024  \nDOI  \n10.17219/acem/184040  \nCopyright  \nCopyright by Author(s)  \nThis is an article distributed under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0)([https://creativecommons.org/licenses/by/3.0/](https://creativecommons.org/licenses/by/3.0/))  \nAbstract  \nPrevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FSin patients could be more satisfactory. Healthcare personnel in clinical settings use a combination of tests and self-reporting to diagnose patients and those at risk of frailty, which is time-consuming and costly. Modern medicine uses artificial intelligence (AI) to study the physical and psychosocial domains of frailty in cardiac patients with HF. This paper aims to present the potential of using the AI approach, emphasizing machine learning (ML) in predicting frailty in patients with HF. Our team reviewed the literature on ML applications for FS and reviewed frailty measurements applied to modern clinical practice. Our approach analysis resulted in recommendations of ML algorithms for predicting frailty in patients. We also present the exemplary application of ML for FS in patients with HF based on the Tilburg Frailty Indicator (TFI) questionnaire, taking into account psychosocial variables.  \nKey words: heart failure, medical personnel, machine learning, frailty syndrome, artificial intelligence  \nCite as  \nSzczepanowski R, Uchmanowicz I, Pasieczna AH, ","cbCaitbJsZQcpFoz","https://ap.wps.com/l/cbCaitbJsZQcpFoz","pdf",227770,1,7,"English","en",105,"# Abstract\n# Introduction\n## Frailty syndrome definition and clinical relevance\n## Frailty in cardiology and its association with heart failure","[{\"question\":\"Why is frailty syndrome prevention and diagnosis in heart failure challenging?\",\"answer\":\"Clinicians often rely on combinations of tests and self-reporting, which are time-consuming and costly. Traditional diagnostic approaches may be less satisfactory for efficiently tailoring care.\"},{\"question\":\"How does the paper position AI and machine learning in frailty prediction?\",\"answer\":\"It presents the potential of AI approaches and emphasizes machine learning to predict frailty in patients with heart failure by analyzing physical and psychosocial domains.\"},{\"question\":\"What example does the paper provide for applying machine learning to predict frailty?\",\"answer\":\"It demonstrates an exemplary application using the Tilburg Frailty Indicator (TFI) questionnaire while accounting for psychosocial variables.\"}]","Application of machine learning in predicting frailty syndrome in patients with heart failure | PDF",1785680726,18,{"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},"application-of-machine-learning-in-predicting-frailty-syndrome-in-patients-with-heart-failure","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-in-predicting-frailty-syndrome-in-patients-with-heart-failure/118008/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is frailty syndrome prevention and diagnosis in heart failure challenging?","Question",{"text":75,"@type":76},"Clinicians often rely on combinations of tests and self-reporting, which are time-consuming and costly. Traditional diagnostic approaches may be less satisfactory for efficiently tailoring care.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper position AI and machine learning in frailty prediction?",{"text":80,"@type":76},"It presents the potential of AI approaches and emphasizes machine learning to predict frailty in patients with heart failure by analyzing physical and psychosocial domains.",{"name":82,"@type":73,"acceptedAnswer":83},"What example does the paper provide for applying machine learning to predict frailty?",{"text":84,"@type":76},"It demonstrates an exemplary application using the Tilburg Frailty Indicator (TFI) questionnaire while accounting for psychosocial variables.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]