Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach

• Published in: Journal of Medical Internet Research Vol. 22; no. 6; p. e16213
• Main Authors: Peng, Li-Ning, Hsiao, Fei-Yuan, Lee, Wei-Ju, Huang, Shih-Tsung, Chen, Liang-Kung
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications Inc 11.06.2020; JMIR Publications
• Subjects: Aged; Aged, 80 and over; Computer applications to medicine. Medical informatics; Female; Frail Elderly; Frail Elderly - psychology; Frailty; Frailty - psychology; Humans; Machine Learning; Male; Multimorbidity; Multimorbidity - trends; Original Paper; Public aspects of medicine; R858-859.7; RA1-1270; Retrospective Studies; Risk Factors; mortality; multimorbidity frailty index; intensive care unit admissions; unplanned hospitalizations; random forest; machine learning

Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-r...