Leveraging deep learning and clinical notes for surveillance and prediction of intentional self-harm and suicide

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PROJECT SUMMARY: Suicide is one of the leading causes of death in the United States, with more than 47,000 individuals dying by suicide each year. The identification of individuals at risk for suicide is an important step for a comprehensive approach to suicide prevention. Despite extensive research on risk factors for intentional self-harm and suicide, prospective prediction of suicide remains a difficult task with poor predictive power. Recent studies suggest that new machine learning methods applied to electronic health records (EHR) show promising results. However, more advanced computational approaches such as deep learning, have not been fully leveraged in this field, especially in the area of advanced methods for text classification of clinical notes. Our aims in this project, are to improve the phenotyping of suicidal behavior, and the prediction of future suicidal behavior and suicide deaths by integrating mortality data with EHR data and leveraging state-of-the-art natural language computational approaches. We will also investigate methods for explain ability and interpretability of the models to improve future adoption by clinicians. We will validate our models by examining reproducibility and generalizability across two health systems using similar data at both sites.