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Application of machine learning in predicting frailty syndrome in patients with heart failure

Szczepanowski, Remigiusz; Uchmanowicz, Izabella; Pasieczna-Dixit, Aleksandra H.; Sobecki, Janusz; Katarzyniak, Radoslaw; Kołaczek, Grzegorz; Lorkiewicz, Wojciech; Kędras, Maja; Dixit, Anant; Biegus, Jan; Wleklik, Marta; Gobbens, Robbert J.J.; Hill, Loreena; Jaarsma, Tiny; Hussain, Amir; Barbagallo, Mario; Veronese, Nicola; Morabito, Francesco C.; Kahsin, Aleksander

Authors

Remigiusz Szczepanowski

Izabella Uchmanowicz

Aleksandra H. Pasieczna-Dixit

Janusz Sobecki

Radoslaw Katarzyniak

Grzegorz Kołaczek

Wojciech Lorkiewicz

Maja Kędras

Anant Dixit

Jan Biegus

Marta Wleklik

Robbert J.J. Gobbens

Loreena Hill

Tiny Jaarsma

Mario Barbagallo

Nicola Veronese

Francesco C. Morabito

Aleksander Kahsin



Abstract

Prevention 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 FS in 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.

Journal Article Type Article
Acceptance Date Feb 13, 2024
Online Publication Date Mar 26, 2024
Publication Date 2024
Deposit Date Apr 2, 2024
Publicly Available Date Apr 2, 2024
Journal Advances in Clinical and Experimental Medicine
Print ISSN 1899–5276
Publisher Wroclaw Medical University
Peer Reviewed Peer Reviewed
Volume 33
Issue 3
Pages 309-315
DOI https://doi.org/10.17219/acem/184040
Keywords heart failure, medical personnel, machine learning, frailty syndrome, artificial intelligence
Public URL http://researchrepository.napier.ac.uk/Output/3579747

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