Can suicide theory-guided natural language processing of clinical progress notes improve existing prediction models of Veteran suicide mortality?

背景：减少美国退伍军人的自杀和自杀企图是一个主要的国家优先事项， 每年有6,000多名退伍军人死于自杀，还有更多的人试图自杀。2017年，最新 在有数据可查的年份，退伍军人的自杀率是非退伍军人的1.5倍， 女性退伍军人的自杀率是非退伍军人女性的2.2倍。当前VHA自杀 风险预测模型遭受大量的假阴性-退伍军人不被视为高风险 自杀未遂或自杀身亡者。这些自杀预测模型并没有将富人 来自临床进展记录的信息可能会提高我们预测自杀行为的能力。这在很大程度 临床进展记录中的信息是非结构化的自由文本。自杀专用本体和信息 可以从非结构化临床进展记录中提取自杀相关信息的提取系统 available. 意义/影响：提高VHA识别最有可能企图自杀的退伍军人的能力 确保有限的干预资源可以集中在风险最高的退伍军人身上， 企图自杀或死于自杀。拟议的研究与HSR&D研究的优先事项保持一致， VA领导层制定了2018 - 2024年VA战略目标，将自杀预防列为“VA的 最高临床优先级” 创新：我们的关键方法创新是将最先进的理论框架（3步 自杀理论）来预测谁最有可能对他们的自杀想法采取行动与国家的最先进的数据 科学方法（NLP，机器学习）。因为我们的自杀理论概念，那就是绝望， 连通性，心理痛苦和自杀能力，在结构化的患者数据中没有表现出来，我们 将开发新的NLP和信息提取工具，并将其应用于临床进展记录， 其中的一部分还没有被完全征收，以改善自杀预测模型。 具体目标：我们有三个具体目标： 1.开发一个自杀专用的本体，用于机器识别绝望，连通性， 心理疼痛，以及与退伍军人临床接触的进展记录中的自杀能力， 企图自杀或死于自杀。 2.提取关于绝望、连通性、心理痛苦的存在和强度的信息， 和自杀的能力，并描述这些概念的变化， 自杀或企图自杀。 3.确定绝望，连通性，心理痛苦和能力的预测有效性 关于退伍军人自杀企图和死亡率的两个预测模型，VA目前 临床应用：STORM和REACHVET。 方法学：拟定的混合方法研究采用探索性序贯设计， 这一部分（目标1）为定量分析（目标2和3）提供了信息。数据收集将来自现有临床 在VHA的企业数据仓库，VA的自杀预防应用网络和来自 退伍军人事务部和国防部的自杀数据库我们将使用语言注释和主题分析为目标1， 目标2和3的自然语言处理和机器学习模型。目标人群是退伍军人 通过VHA接受护理。 下一步/实施：我们最重要的下一步是定期与当地和国家 退伍军人事务部心理健康和自杀预防办公室（OMHSP）的同事，以促进实施 我们在STORM和REACHVET的操作版本中的结果。

Background: Reducing suicide and suicide attempts among U.S. Veterans is a major national priority, as more than 6,000 Veterans die by suicide every year and many more attempt suicide. In 2017, the most recent year for which data are available, the suicide rate among Veterans was 1.5 times the rate of non-Veterans, and the suicide rate among female Veterans was 2.2 times the rate of non-Veteran females. Current VHA suicide risk prediction models suffer from high numbers of false negatives - Veterans not deemed at high risk of suicide who do attempt or die by suicide. These suicide prediction models have not incorporated the rich information from clinical progress notes that may improve our ability to predict suicidal behavior. Much of this information in clinical progress notes is unstructured free text. A suicide-specific ontology and information extraction system that can extract suicide-related information from unstructured clinical progress notes is not available. Significance/Impact: Enhancing VHA's ability to identify Veterans who are most likely to attempt suicide ensures that limited intervention resources can be focused on Veterans with the highest risk, before they attempt suicide or die by suicide. The proposed study is well-aligned with priorities for HSR&D research and with VA strategic goals for 2018 – 2024 set out by VA leadership, who listed suicide prevention as “VA's highest clinical priority.” Innovation: Our key methodological innovation is to pair a state-of-the-art theoretical framework (3-step Theory of Suicide) to predict who is most likely to act on their suicidal thoughts with state-of-the-art data science methods (NLP, machine learning). Since our suicide-theory concepts, that is hopelessness, connectedness, psychological pain, and capacity for suicide, are not represented in structured patient data, we will develop novel NLP and information extraction tools and apply them to clinical progress notes, the potential of which has not been fully levied to improve suicide prediction models. Specific Aims: We have three specific aims: 1. Develop a suicide-specific ontology for machine recognition of hopelessness, connectedness, psychological pain, and capacity for suicide in progress notes of clinical encounters with Veterans who attempted or died by suicide. 2. Extract information on the presence and intensity of hopelessness, connectedness, psychological pain, and capacity for suicide in clinical progress notes and describe change in these concepts in proximity of a suicide or suicide attempt. 3. Determine the predictive validity of hopelessness, connectedness, psychological pain, and capacity for suicide regarding Veteran suicide attempts and mortality in two prediction models that VA currently uses in clinical practice: STORM and REACHVET. Methodology: The proposed mixed-methods study has an exploratory sequential design where a qualitative component (Aim 1) informs quantitative analyses (Aims 2 and 3). Data collection will be from existing clinical progress notes in VHA's Corporate Data Warehouse, VA's Suicide Prevention Applications Network and from the VA/DoD Suicide Data Repository. We will use linguistic annotation and thematic analysis for Aim 1 and natural language processing and machine learning models for Aims 2 and 3. The target population is Veterans who receive care through VHA. Next Steps/Implementation: Our most important next step is to be in regular contact with local and national colleagues at the VA Office of Mental Health and Suicide Prevention (OMHSP) to facilitate implementation of our results in the operational versions of STORM and REACHVET.