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

To estimate the proportion of Veterans with ever self-harm using a novel positive and unlabeled learning algorithm applied to electronic health records.

Key Findings:
  • Veterans account for nearly 14% of adult suicide deaths despite being only 7.6% of the population.
  • The unadjusted suicide rate among Veterans is approximately double that of non-Veteran adults.
  • Undercoding of mental health conditions, including self-harm, is prevalent in electronic health records.
Interpretation:

The study discusses the challenges of underreporting and undercoding in electronic health records.

Limitations:
  • The study focuses on ever self-harm as a phenotype, which may differ from operational surveillance that targets recent suicidal behaviors.
  • Potential biases in classification due to undercoding and the nature of the data used.
Conclusion:

The application of PULSNAR may improve the identification of self-harm in Veterans' health records.

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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