Detecting Uncoded Self-Harm in Veterans’ Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study - Takeaways - MDSpire

Detecting Uncoded Self-Harm in Veterans’ Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study

  • By

  • Praveen Kumar

  • Alexandria D Viszolay

  • Rajesh Upadhayaya

  • Fariha Moomtaheen

  • Donald R Greer

  • Cristian G Bologa

  • Kristan A Schneider

  • Sharon E Davis

  • Michael E Matheny

  • David van der Goes

  • Gerardo Villarreal

  • Yiliang Zhu

  • Mauricio Tohen

  • Scott A Malec

  • Jeremy J Yang

  • Elliot M Fielstein

  • Christophe Gerard Lambert

  • June 4, 2026

  • 0 min

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  • 1

    Suicide and self-harm are major public health issues in the U.S., with suicide being the second leading cause of death for individuals aged 10-34 years.

  • 2

    Veterans represent 14% of adult suicide deaths, with their suicide rate approximately double that of non-Veteran adults.

  • 3

    Co-occurring conditions like PTSD and depression significantly increase the risk of self-harm and suicide among Veterans.

  • 4

    Undercoding of mental health conditions in electronic health records limits accurate reporting and effective intervention strategies.

  • 5

    A novel positive and unlabeled learning algorithm was applied to Veterans' EHR data to estimate the prevalence of self-harm.

Original Source(s)

Detecting Uncoded Self-Harm in Veterans’ Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study

  • by Praveen Kumar, Alexandria D Viszolay, Rajesh Upadhayaya, Fariha Moomtaheen, Donald R Greer, Cristian G Bologa, Kristan A Schneider, Sharon E Davis, Michael E Matheny, David van der Goes, Gerardo Villarreal, Yiliang Zhu, Mauricio Tohen, Scott A Malec, Jeremy J Yang, Elliot M Fielstein, Christophe Gerard Lambert

  • June 4, 2026

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