Validation of an Electronic Health Record–Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems

Key Points

Question: Can a process for training machine-learning algorithms based on electronic health records identify individuals at increased risk of suicide attempts across independent health care systems?

Findings: In this prognostic study, using a supervised learning approach applied to structured electronic health record data from more than 3.7 million patients across 5 diverse US health care systems, models detected a mean of 38% of cases of suicide attempt with 90% specificity a mean of 2.1 years in advance.

Meaning: These findings suggest that a computationally efficient machine-learning approach leveraging the full spectrum of structured electronic health record data may be able to detect the risk of suicidal behavior in unselected patients and may facilitate the development of clinical decision support tools that inform risk reduction interventions.