Improving Suicide Prediction using NLP-Extracted Social Determinants of Health Suicide is among the leading causes of death worldwide and among United States Veterans in particular. Current methods of risk assessment are limited in their ability to accurately identify patients who are at the highest risk of suicide. The overarching goal of this proposal is to strengthen suicide prediction efforts by gaining a more granular understanding of the association between social determinants of health and suicide risk. Social determinants of health (SDH) refer to the conditions in which people are born, live, work, and age. A number of SDH are known risk factors for suicide. While SDH could be obtained from the structured EHR data, their scope is limited. A recent study has shown that EHR notes contain about 90 times more information about SDH than the structured data. To address this gap, we propose a stepwise approach that leverages the power of EHR and new computational methdologies to explore associations between natural language processing extracted SDH and suicide ideation, attempt and death. This approach is critical to the development of next- generation suicide prevention tools.

Improving Suicide Prediction using NLP-Extracted Social Determinants of Health Suicide is among the leading causes of death worldwide and among United States Veterans in particular. Current methods of risk assessment are limited in their ability to accurately identify patients who are at the highest risk of suicide. The overarching goal of this proposal is to strengthen suicide prediction efforts by gaining a more granular understanding of the association between social determinants of health and suicide risk.