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Documentation of Nursing Diagnosis

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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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 MexicoMSC10 5550, Albuquerque, US.

Journal of Medical Internet Research

|April 19, 2026

Summary

This summary is machine-generated.

A novel algorithm identified thousands of U.S. Veterans with self-harm events missed by diagnostic codes. This Positive and Unlabeled (PU) learning approach estimates true prevalence, aiding early intervention for mental health conditions.

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Area of Science:

Public Health
Data Science
Mental Health Research

Background:

Suicide and self-harm are critical public health issues in the U.S.
Undercoding of mental health conditions in electronic health records (EHRs) leads to missing data.
Lack of reliable negative examples hinders accurate prevalence estimation and identification of at-risk individuals.

Purpose of the Study:

To identify U.S. Veterans with self-harm events not captured by diagnostic codes in EHRs.
To estimate the true prevalence of self-harm among Veterans using a novel Positive and Unlabeled (PU) learning algorithm.
To address undetected mental health diagnoses within large healthcare datasets.

Main Methods:

Retrospective observational study of 1.3 million Veterans Health Administration EHRs (1999-2019).
Application of the PULSNAR (Positive Unlabeled Learning Selected Not At Random) algorithm to estimate uncoded self-harm.
Independent chart reviews of 97 uncoded Veterans to validate algorithm predictions and refine 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.
Analysis suggests coded self-harm represents only 23.4% of all documented self-harm events in Veterans.

Conclusions:

PULSNAR offers a scalable framework for estimating mental health condition prevalence and identifying uncoded cases without requiring negative labels.
This method addresses diagnostic undercoding in EHRs, improving prevalence estimation and supporting targeted interventions.
The approach can enhance clinical decision support, resource allocation, and research for better mental health outcomes.