Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults.

Journal: The journals of gerontology. Series A, Biological sciences and medical sciences
PMID: 32498077

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.

Authors

  • Jaime Lynn Speiser
    Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
  • Kathryn E Callahan
    Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
  • Denise K Houston
    Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
  • Jason Fanning
    Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina.
  • Thomas M Gill
    Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT.
  • Jack M Guralnik
    Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore.
  • Anne B Newman
    Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pennsylvania.
  • Marco Pahor
    Department of Aging and Geriatric Research, University of Florida, Gainesville.
  • W Jack Rejeski
    Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina.
  • Michael E Miller
    Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.

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