Development and validation of a novel model for characterizing migraine outcomes within real-world data.

Journal: The journal of headache and pain
PMID: 36131249

Abstract

BACKGROUND: In disease areas with 'soft' outcomes (i.e., the subjective aspects of a medical condition or its management) such as migraine or depression, extraction and validation of real-world evidence (RWE) from electronic health records (EHRs) and other routinely collected data can be challenging due to how the data are collected and recorded. In this study, we aimed to define and validate a scalable framework model to measure outcomes of migraine treatment and prevention by use of artificial intelligence (AI) algorithms within EHR data.

Authors

  • Nada A Hindiyeh
    Stanford Headache Clinic at Hoover Pavilion, Stanford, CA, USA.
  • Daniel Riskin
    Verantos, Inc., Menlo Park, CA, USA.
  • Kimberly Alexander
    Verantos, Inc., Menlo Park, CA, USA.
  • Roger Cady
    Lundbeck LLC, Deerfield, IL, USA.
  • Steven Kymes
    Lundbeck LLC, Deerfield, IL, USA. SKYM@lundbeck.com.

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