title: Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.
abstract: OBJECTIVE
  To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics.
  
  
  METHODS
  Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables.
  
  
  RESULTS
  The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey.
  
  
  CONCLUSIONS
  The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.
authors.affiliation: Department of Physical Therapy, University of Alberta, 2-50 Corbett Hall, Edmonton, AB, T6G 2G4, Canada, dgross@ualberta.ca.
authors.forename: Douglas P
authors.initials: DP
authors.lastname: Gross
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.forename: Ivan
authors.initials: I
authors.lastname: Steenstra
authors.forename: Susan
authors.initials: S
authors.lastname: Barnsley
authors.forename: Calvin
authors.initials: C
authors.lastname: Haws
authors.forename: Tyler
authors.initials: T
authors.lastname: Amell
authors.forename: Greg
authors.initials: G
authors.lastname: McIntosh
authors.forename: Juliette
authors.initials: J
authors.lastname: Cooper
authors.forename: Osmar
authors.initials: O
authors.lastname: Zaiane
mesh_terms: D000328:Adult; D000416:Alberta; D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification / Q000379:methods; D020000:Decision Support Systems, Clinical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009140:Musculoskeletal Diseases / Q000145:classification* / Q000534:rehabilitation*; D060051:Occupational Injuries / Q000145:classification* / Q000534:rehabilitation*; D062707:Return to Work; D012985:Software Design