A Scoping Review on the Use of Machine Learning in Return-to-Work Studies: Strengths and Weaknesses.

Journal: Journal of occupational rehabilitation
PMID: 37378718

Abstract

PURPOSE: Decisions to increase work participation must be informed and timely to improve return to work (RTW). The implementation of research into clinical practice relies on sophisticated yet practical approaches such as machine learning (ML). The objective of this study is to explore the evidence of machine learning in vocational rehabilitation and discuss the strengths and areas for improvement in the field.

Authors

  • Reuben Escorpizo
    Department of Rehabilitation and Movement Science, University of Vermont, College of Nursing and Health Sciences, Burlington, VT, USA.
  • Georgios Theotokatos
    Department of Rehabilitation and Movement Science, College of Nursing and Health Sciences, University of Vermont, 106 Carrigan Dr, Burlington, VT, 05405, USA.
  • Carole A Tucker
    School of Health Professions, University of Texas- Medical Branch, Galveston, TX, USA.

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