Predicting Future Mobility Limitation in Older Adults: A Machine Learning Analysis of Health ABC Study Data.

Journal: The journals of gerontology. Series A, Biological sciences and medical sciences
PMID: 34529794

Abstract

BACKGROUND: Mobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting future mobility limitation in older adults using repeated measures data.

Authors

  • Jaime L Speiser
    Dept. of Public Health Sciences, Medical University of South Carolina, USA.
  • Kathryn E Callahan
    Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
  • Edward H Ip
    Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
  • Michael E Miller
    Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
  • Janet A Tooze
    Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
  • Stephen B Kritchevsky
    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.

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