Machine Learning Approaches for the Frailty Screening: A Narrative Review

Eduarda Oliosi, Federico Guede-Fernández and Ana Londral
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Eduarda Oliosi: Value for Health CoLAB, 1150-190 Lisboa, Portugal
Federico Guede-Fernández: Value for Health CoLAB, 1150-190 Lisboa, Portugal
Ana Londral: Value for Health CoLAB, 1150-190 Lisboa, Portugal

IJERPH, 2022, vol. 19, issue 14, 1-11

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.

Keywords: frailty; indicators; screening; artificial intelligence; healthcare (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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