Researchers develop a machine learning method to reveal self-harm history hidden in veterans' electronic medical records.
Key Details
- 1Study analyzed over 1.3 million Veterans Health Administration patients' electronic records.
- 2Diagnosis codes identified only 1.85% with self-harm history; machine learning estimates true rate at 7.9%.
- 3The PULSNAR algorithm addressed missing or incomplete coding of self-harm in standard record fields.
- 4Problem lists captured self-harm for just 22.6% of those with relevant diagnosis codes.
- 5Findings show a substantial gap in the visibility of clinically important mental health history.
- 6This method is currently a research tool and not yet ready for direct clinical deployment.
Why It Matters
Uncovering hidden self-harm history in health records can significantly improve mental health care planning and research. For radiology-AI and broader medical AI communities, these advances in handling undercoded conditions demonstrate how informatics tools can support more accurate predictive models and population health analytics.
Source
EurekAlert
Related News
•EurekAlert
UVA's $4M NIH Grant Backs AI to Optimize Cardiac MRI Therapies
UVA secures $4 million NIH funding to develop GenStrain, an AI model using MRI data to personalize cardiac pacing therapies.
•EurekAlert
Machine Learning Models Predict Cancer Risk with Routine Health Data
Researchers developed machine learning models using routinely collected health data to predict individual risk for 20 common cancers, outperforming traditional methods for several cancer types.
•EurekAlert
Duke Receives $25M to Lead National Trustworthy AI Hub for Dementia and Aging
Duke University has launched a $25 million national center to make AI for aging and dementia clinically robust and trustworthy.