Imputation and characterization of uncoded self-harm in major mental illness using machine learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID:

Abstract

OBJECTIVE: We aimed to impute uncoded self-harm in administrative claims data of individuals with major mental illness (MMI), characterize self-harm incidence, and identify factors associated with coding bias.

Authors

  • Praveen Kumar
    Center for Global Health, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA.
  • Anastasiya Nestsiarovich
    Center for Global Health, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA.
  • Stuart J Nelson
    George Washington University, Washington, DC, DC, United States.
  • Berit Kerner
    Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
  • Douglas J Perkins
    Center for Global Health, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA.
  • Christophe G Lambert
    Center for Global Health, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA.