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

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

doi:10.17219/acem/184040

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

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Pages (from-to)	309-315
Number of pages	7
Journal	Advances in Clinical and Experimental Medicine
Volume	33
Issue number	3
DOIs	https://doi.org/10.17219/acem/184040
Publication status	Published - 26 Mar 2024
Externally published	Yes

Keywords

artificial intelligence
frailty syndrome
heart failure
machine learning
medical personnel

ASJC Scopus subject areas

Internal Medicine
Medicine (miscellaneous)
Reviews and References, Medical
General Biochemistry,Genetics and Molecular Biology
Genetics(clinical)
Pharmacology (medical)

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10.17219/acem/184040Licence: CC BY

Application of machine learning in predicting frailty syndrome in patients with heart failure
Copyright 2024 the authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 222 KBLicence: CC BY

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Szczepanowski, R., Uchmanowicz, I., Pasieczna-Dixit, A. H., Sobecki, J., Katarzyniak, R., Kołaczek, G., Lorkiewicz, W., Kędras, M., Dixit, A., Biegus, J., Wleklik, M., Gobbens, R. J. J., Hill, L., Jaarsma, T., Hussain, A., Barbagallo, M., Veronese, N., Morabito, F. C., & Kahsin, A. (2024). Application of machine learning in predicting frailty syndrome in patients with heart failure. Advances in Clinical and Experimental Medicine, 33(3), 309-315. https://doi.org/10.17219/acem/184040

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title = "Application of machine learning in predicting frailty syndrome in patients with heart failure",

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.",

keywords = "artificial intelligence, frailty syndrome, heart failure, machine learning, medical personnel",

author = "Remigiusz Szczepanowski and Izabella Uchmanowicz and Pasieczna-Dixit, {Aleksandra H.} and Janusz Sobecki and Rados{\l}aw Katarzyniak and Grzegorz Ko{\l}aczek and Wojciech Lorkiewicz and Maja K{\c e}dras and Anant Dixit and Jan Biegus and Marta Wleklik and Gobbens, {Robbert J.J.} and Loreena Hill and Tiny Jaarsma and Amir Hussain and Mario Barbagallo and Nicola Veronese and Morabito, {Francesco C.} and Aleksander Kahsin",

year = "2024",

month = mar,

day = "26",

doi = "10.17219/acem/184040",

language = "English",

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Szczepanowski, R, Uchmanowicz, I, Pasieczna-Dixit, AH, Sobecki, J, Katarzyniak, R, Kołaczek, G, Lorkiewicz, W, Kędras, M, Dixit, A, Biegus, J, Wleklik, M, Gobbens, RJJ, Hill, L, Jaarsma, T, Hussain, A, Barbagallo, M, Veronese, N, Morabito, FC & Kahsin, A 2024, 'Application of machine learning in predicting frailty syndrome in patients with heart failure', Advances in Clinical and Experimental Medicine, vol. 33, no. 3, pp. 309-315. https://doi.org/10.17219/acem/184040

TY - JOUR

T1 - Application of machine learning in predicting frailty syndrome in patients with heart failure

AU - Szczepanowski, Remigiusz

AU - Uchmanowicz, Izabella

AU - Pasieczna-Dixit, Aleksandra H.

AU - Sobecki, Janusz

AU - Katarzyniak, Radosław

AU - Kołaczek, Grzegorz

AU - Lorkiewicz, Wojciech

AU - Kędras, Maja

AU - Dixit, Anant

AU - Biegus, Jan

AU - Wleklik, Marta

AU - Gobbens, Robbert J.J.

AU - Hill, Loreena

AU - Jaarsma, Tiny

AU - Hussain, Amir

AU - Barbagallo, Mario

AU - Veronese, Nicola

AU - Morabito, Francesco C.

AU - Kahsin, Aleksander

PY - 2024/3/26

Y1 - 2024/3/26

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - frailty syndrome

KW - heart failure

KW - machine learning

KW - medical personnel

U2 - 10.17219/acem/184040

DO - 10.17219/acem/184040

M3 - Article

C2 - 38530317

AN - SCOPUS:85188920821

SN - 1899-5276

VL - 33

SP - 309

EP - 315

JO - Advances in Clinical and Experimental Medicine

JF - Advances in Clinical and Experimental Medicine

IS - 3

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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