A scoping review of machine learning models to predict risk of falls in elders, without using sensor data.

Journal: Diagnostic and prognostic research
Published Date: May 6, 2025

Abstract

OBJECTIVES: This scoping review assesses machine learning (ML) tools that predicted falls, relying on information in health records without using any sensor data. The aim was to assess the available evidence on innovative techniques to improve fall prevention management.

Authors

  • Angelo Capodici
    Department of Health Management (Direzione Sanitaria), IRCCS Istituto Ortopedico Rizzoli, Bologna, 40127, Italy.
  • Claudio Fanconi
    Department of Electrical Engineering and Information Technology, ETH Zurich, Zurich, Switzerland.
  • Catherine Curtin
    Department of Surgery, Veterans' Affairs Palo Alto Healthcare System, Palo Alto, CA, USA.
  • Alessandro Shapiro
    Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, USA.
  • Francesca Noci
    Interdisciplinary Research Center for Health Science, Sant'Anna School of Advanced Studies, Pisa, 56127, Italy.
  • Alberto Giannoni
    Cardiology and Cardiovascular Medicine Department, Fondazione Toscana G. Monasterio, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy.
  • Tina Hernandez-Boussard
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.

Keywords

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