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

Journal of Medical Internet Research
Li-Ning PengLiang-Kung Chen

Abstract

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-response relationships remain major challenges in clinical practice. This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. In this study, we used Taiwan's National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overla...Continue Reading

References

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Jul 20, 2007·The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences·Kenneth Rockwood, Arnold Mitnitski
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Jul 10, 2019·Critical Care : the Official Journal of the Critical Care Forum·Zsolt ZadorNophar Geifman

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Citations

Feb 7, 2021·Archives of Gerontology and Geriatrics·Liang-Kung Chen
Mar 7, 2021·Journal of Clinical Medicine·Ljiljana Trtica MajnarićAndreas Holzinger
Sep 27, 2020·Archives of Gerontology and Geriatrics·Liang-Kung Chen

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Software Mentioned

randomForest
R Foundation for Statistical Computing
R

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