Improving Suicide Prediction using NLP-Extracted Social Determinants of Health

使用NLP提取的健康社会决定因素改进自杀预测 自杀是世界范围内的主要死亡原因之一，在#年美国退伍军人中 很特别。目前的风险评估方法在准确识别的能力上是有限的 自杀风险最高的患者。这项提议的首要目标是 通过更细致地了解自杀事件，加强自杀预测工作 健康的社会决定因素与自杀风险之间的关系。 健康的社会决定因素(SDH)指的是人们出生、生活、工作、 和年龄。一些SDH是已知的自杀危险因素。同时可以获得SDH 从结构化的电子病历数据来看，它们的范围是有限的。最近的一项研究表明，EHR注意到 包含的有关SDH的信息大约是结构化数据的90倍。要解决这个问题 GAP，我们提出了一种利用EHR和新的计算能力的分步方法 探索自然语言处理提取的SDH和SDH之间的关联的方法 自杀念头、企图和死亡。这种方法对下一步的发展至关重要- 世代自杀预防工具。

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.