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
AI Pathology Tool SÉMIL Improves Stage II Bowel Cancer Risk Assessment
A La Trobe University-developed AI tool accurately predicts relapse risk in stage II bowel cancer using digital pathology images and descriptions.
•EurekAlert
AI-Guided Handheld Cardiac Ultrasound Reduces Referrals and Costs in Spain
AI-guided handheld cardiac ultrasound enables primary care physicians to detect heart failure, reducing specialist referrals and saving costs.
•EurekAlert
AI Tool Predicts Which Rectal Cancer Patients Benefit from Intensive Therapy
UCL researchers developed an AI that analyzes biopsy slides to identify rectal cancer patients who benefit from adding irinotecan to standard chemoradiotherapy.