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

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.

Citation

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., 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

• 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