Predictive Modeling For Falls

Predictive Modeling for Falls is a computational method that distinguishes itself within the broader class of fall prevention strategies by employing statistical or machine learning techniques to identify individuals at increased risk of falling through the analysis of clinical, biomechanical, and environmental variables.

261

Publications

13.7K

Citations

1.3K

Authors

579

Institutions

Publications per year

2017–2026

128

Authors

1.3K

  1. SR
    Stephen R. Lord

    Neuroscience Research Australia, Australia

    8 publications · 497 citations

    8

    h-index

  2. CS
    Catherine Sherrington

    The University of Sydney, Australia

    5 publications · 435 citations

    5

    h-index

  3. JC
    Jacqueline Close

    Neuroscience Research Australia, Australia

    4 publications · 394 citations

    4

    h-index

  4. GM
    Gammon M. Earhart

    Washington University in St. Louis, United States

    4 publications · 203 citations

    4

    h-index

  5. CG
    Colleen G. Canning

    The University of Sydney, Australia

    4 publications · 404 citations

    4

    h-index

1–5 of 1.3K

Venues

  1. Sensors

    6 publications · 211 citations

    6

    h-index

  2. PLoS ONE

    6 publications · 295 citations

    6

    h-index

  3. Journal of the American Medical Directors Association

    JAMDA · 6 publications · 311 citations

    6

    h-index

  4. 5

    h-index

  5. Scientific Reports

    5 publications · 394 citations

    5

    h-index

1–5