Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records

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Emir B ; Masters ET ; Mardekian J ; Clair A ; Kuhn M ; Silverman SL (2015)
  • Publisher: Dove Medical Press
  • Journal: Journal of Pain Research, volume 8, pages 277-288 (issn: 1178-7090, eissn: 1178-7090)
  • Related identifiers: pmc: PMC4467741, doi: 10.2147/JPR.S82566, doi: 10.2147/JPR.S8256
  • Subject: electronic medical records | fibromyalgia | R5-920 | predictive modeling | health care resource utilization | Medicine (General) | Original Research | real-world data | random forest | Journal of Pain Research

Birol Emir,1 Elizabeth T Masters,1 Jack Mardekian,1 Andrew Clair,1 Max Kuhn,2 Stuart L Silverman,3 1Pfizer Inc., New York, NY, 2Pfizer Inc., Groton, CT, 3Cedars-Sinai Medical Center, Los Angeles, CA, USA Background: Diagnosis of fibromyalgia (FM), a chronic musculoskel... View more
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