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  1. Home
  3. Detecting Uncoded Self-harm In Veterans' Electronic Health Records Using Positive And Unlabeled Learning: Retrospective Cohort Study.
  1. Home
  3. Detecting Uncoded Self-harm In Veterans' Electronic Health Records Using Positive And Unlabeled Learning: Retrospective Cohort Study.

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Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning:

Praveen Kumar1, Alexandria D Viszolay1, Rajesh Upadhayaya1

  • 1Department of Internal Medicine, School of Medicine, University of New Mexico Health Sciences Center, 1 University of New Mexico, MSC10 5550, Albuquerque, NM, 87131, United States, 1 505-272-9709.

Journal of Medical Internet Research
|June 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A novel algorithm identified significant undercoding of self-harm events in US Veterans

Keywords:
PULSNARVeterans' healthelectronic health recordmachine learningpositive unlabeled learning selected not at randomself-injurious behavior

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Area of Science:

  • Health Informatics
  • Mental Health Research
  • Machine Learning Applications

Background:

  • Undercoding of mental health conditions, especially self-harm, is prevalent in healthcare datasets.
  • This data gap hinders accurate predictive modeling and prevalence estimation.
  • Positive and unlabeled (PU) learning offers a solution to identify underdiagnosed cases.

Purpose of the Study:

  • To identify US Veterans with self-harm events missed by diagnostic codes in electronic health records (EHRs).
  • To estimate the true prevalence of self-harm using a novel PU learning algorithm.
  • To address limitations in current healthcare data for mental health research.

Main Methods:

  • Retrospective cohort study of 1.3 million Veterans' EHRs (1999-2019).
  • Application of the PULSNAR (positive unlabeled learning selected not at random) algorithm.
  • Independent expert chart reviews for validation and post hoc calibration of prevalence estimates.

Main Results:

  • Only 1.85% of Veterans had coded self-harm, while PULSNAR estimated an overall prevalence of 10.46%.
  • PULSNAR identified an additional 8.77% of self-harm cases among the uncoded population.
  • Calibrated estimates suggest coded self-harm represents only 23.4% of all documented self-harm events.

Conclusions:

  • PULSNAR provides a scalable framework for estimating mental health condition prevalence, even with undercoding.
  • The method identifies individuals with undocumented cases without needing negative labels.
  • This approach can improve screening, clinical decision support, and resource allocation for mental health care.