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Machine Learning Approaches for the Frailty Screening: A Narrative Review

Author

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  • Eduarda Oliosi

    (Value for Health CoLAB, 1150-190 Lisboa, Portugal
    LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal)

  • Federico Guede-Fernández

    (Value for Health CoLAB, 1150-190 Lisboa, Portugal
    LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal)

  • Ana Londral

    (Value for Health CoLAB, 1150-190 Lisboa, Portugal
    Comprehensive Health Research Center, NOVA Medical School, NOVA University of Lisbon, 1150-082 Lisboa, Portugal)

Abstract

Frailty characterizes a state of impairments that increases the risk of adverse health outcomes such as physical limitation, lower quality of life, and premature death. Frailty prevention, early screening, and management of potential existing conditions are essential and impact the elderly population positively and on society. Advanced machine learning (ML) processing methods are one of healthcare’s fastest developing scientific and technical areas. Although research studies are being conducted in a controlled environment, their translation into the real world (clinical setting, which is often dynamic) is challenging. This paper presents a narrative review of the procedures for the frailty screening applied to the innovative tools, focusing on indicators and ML approaches. It results in six selected studies. Support vector machine was the most often used ML method. These methods apparently can identify several risk factors to predict pre-frail or frailty. Even so, there are some limitations (e.g., quality data), but they have enormous potential to detect frailty early.

Suggested Citation

  • Eduarda Oliosi & Federico Guede-Fernández & Ana Londral, 2022. "Machine Learning Approaches for the Frailty Screening: A Narrative Review," IJERPH, MDPI, vol. 19(14), pages 1-11, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8825-:d:867261
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    References listed on IDEAS

    as
    1. Marek Zak & Tomasz Sikorski & Magdalena Wasik & Daniel Courteix & Frederic Dutheil & Waldemar Brola, 2022. "Frailty Syndrome—Fall Risk and Rehabilitation Management Aided by Virtual Reality (VR) Technology Solutions: A Narrative Review of the Current Literature," IJERPH, MDPI, vol. 19(5), pages 1-12, March.
    2. Giuseppe Liotta & Silvia Ussai & Maddalena Illario & Rónán O’Caoimh & Antonio Cano & Carol Holland & Regina Roller-Winsberger & Alessandra Capanna & Chiara Grecuccio & Mariacarmela Ferraro & Francesca, 2018. "Frailty as the Future Core Business of Public Health: Report of the Activities of the A3 Action Group of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA)," IJERPH, MDPI, vol. 15(12), pages 1-26, December.
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