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Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults.
- Source :
-
The journals of gerontology. Series A, Biological sciences and medical sciences [J Gerontol A Biol Sci Med Sci] 2021 Mar 31; Vol. 76 (4), pp. 647-654. - Publication Year :
- 2021
-
Abstract
- Background: Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty in understanding the complex algorithms that underlie models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability.<br />Method: We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study.<br />Results: Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated using data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest).<br />Conclusions: Machine learning methods offer an alternative to traditional approaches for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.<br /> (© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
- Subjects :
- Aged
Algorithms
Female
Humans
Male
Outcome Assessment, Health Care methods
Outcome Assessment, Health Care trends
Prognosis
Reproducibility of Results
Trauma Severity Indices
Accidental Falls prevention & control
Accidental Injuries etiology
Accidental Injuries prevention & control
Accidental Injuries psychology
Accidental Injuries therapy
Aging physiology
Aging psychology
Clinical Decision Rules
Decision Support Techniques
Machine Learning
Subjects
Details
- Language :
- English
- ISSN :
- 1758-535X
- Volume :
- 76
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- The journals of gerontology. Series A, Biological sciences and medical sciences
- Publication Type :
- Academic Journal
- Accession number :
- 32498077
- Full Text :
- https://doi.org/10.1093/gerona/glaa138