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Evidence

Use of Mutual Information Theory in Development and Refinement of a Predictive Model for Early Identification of Ankylosing Spondylitis

Date: 07/12/2019

Poster presented at: American College of Rheumatology Annual Meeting

Location and date: San Diego, CA; November 3-8, 2017

Authors: Atul Deodhar, MD,Cody Garges, MS,Oodaye Shukla, MS,Theresa Arndt, MLS,Tara Grabowsky, MD,Yujin Park, PharmD

Objectives:

  • To develop a predictive mathematical model for AS based on features observed in the claims histories of patients with and without a diagnosis of AS, to aid in the earlier identification of AS.

Conclusions:

  • Predictive models for AS diagnosis were developed and refined in this US administrative claims database.
  • Application of machine learning dramatically improves the performance of the model in comparison with traditional linear regression or purely clinical models.
  • These predictive models may be vital for a timely diagnosis of AS despite their low PPV.
  • Application of these models in a separate claims database and confirmation of AS diagnosis through in-person medical examinations are ongoing for model validation and use in real-world settings.

Use of Mutual Information Theory in Development and Refinement of a Predictive Model for Early Identification of Ankylosing Spondylitis

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