Machine learning for work disability prevention: introduction to the special series

Douglas P Gross, Ivan A Steenstra, Frank E Harrell Jr, Colin Bellinger, Osmar Zaiane

Research output: Contribution to journalArticleAcademicpeer-review


Rapid development in computer technology has led to sophisticated methods of analyzing large datasets with the aim of improving human decision making. Artificial Intelligence and Machine Learning (ML) approaches hold tremendous potential for solving complex real-world problems such as those faced by stakeholders attempting to prevent work disability. These techniques are especially appealing in work disability contexts that collect large amounts of data such as workers' compensation settings, insurance companies, large corporations, and health care organizations, among others. However, the approaches require thorough evaluation to determine if they add value to traditional statistical approaches. In this special series of articles, we examine the role and value of ML in the field of work disability prevention and occupational rehabilitation.
Original languageEnglish
Pages (from-to)303-307
Number of pages5
JournalJournal of Occupational Rehabilitation
Issue number3
Publication statusPublished - Sept 2020
Externally publishedYes


  • machine learning
  • work disability prevention


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