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

自杀理论指导的临床进展笔记自然语言处理能否改善现有的退伍军人自杀死亡率预测模型？

• 批准号: 10187800
• 负责人: Alex Sox-Harris
• 金额: --
• 依托单位: VETERANS ADMIN PALO ALTO HEALTH CARE SYS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10187800
• 关键词: Age; Area; Attention; Automated Annotation; Caring; Clinical; Data; Data Collection; Data Science; Detection; Development; Electronic Health Record; Ensure; Event; Feeling hopeless; Feeling suicidal; Female; Gender; Goals; Information Retrieval; Intervention; Knowledge; Language; Leadership; Linguistics; Machine Learning; Maps; Medical; Medicine; Mental Health; Methodology; Methods; Modeling; Natural Language Processing; Nomenclature; Ontology; Pain; Patients; Performance; Readability; Research; Resources; Risk Assessment; Risk Behaviors; Risk Factors; Signal Transduction; Structure; Suicide; Suicide attempt; Suicide prevention; System; Systematized Nomenclature of Medicine; Target Populations; Text; Time; Translating; Veterans; Veterans Health Administration; clinical encounter; clinical practice; concept mapping; data repository; data warehouse; design; hands-on learning; high risk; implementation facilitation; improved; innovation; machine learning algorithm; novel; phrases; predictive modeling; psychologic; random forest; reducing suicide; risk prediction model; structured data; suicidal behavior; suicidal morbidity; suicidal risk; suicide mortality; suicide rate; support vector machine; text searching; theories; tool

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.

背景：在美国退伍军人中减少自杀和自杀企图是国家的主要优先事项，因为 每年有6,000多名退伍军人因自杀而死，并自杀。在2017年，最近 有数据可用的年份，退伍军人的自杀率是非退伍军人的1.5倍，并且是 女退伍军人的自杀率是非退伍军人女性率的2.2倍。当前的VHA自杀 风险预测模型遭受了大量的假否定性 - 退伍军人没有被视为高风险的退伍军人 自杀或自杀的自杀。这些自杀预测模型尚未纳入富人 来自临床进度的信息可以提高我们预测自杀行为的能力。其中很多 临床进度注释中的信息是非结构化的免费文本。特定于自杀的本体论和信息 可以从非结构化临床进度提取自杀相关信息的提取系统不是 可用的。 意义/影响力：增强VHA识别最有可能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上，然后 尝试自杀或自杀自杀。拟议的研究与HSR＆D研究的优先级相结合， 弗吉尼亚州领导人提出的2018 - 2024年VA战略目标，后者将自杀预防列为“ VA” 最高的临床优先级。” 创新：我们的关键方法论创新是配对最先进的理论框架（三步 自杀理论）预测谁最有可能使用最先进的数据采取自杀思想 科学方法（NLP，机器学习）。由于我们的自杀理论概念，这是绝望的， 在结构化患者数据中未代表联系，心理疼痛和自杀能力，我们 将开发新颖的NLP和信息提取工具，并将其应用于临床进度注释，潜力 其中尚未完全征收改善自杀预测模型。 具体目的：我们有三个具体目标： 1。开发一个特定自杀的本体论，以识别绝望，联系， 心理痛苦和自杀能力在与退伍军人的临床相遇进展情况中 自杀未遂或死亡。 2。提取有关绝望，联系，心理痛苦的存在和强度的信息 和在临床进度注释中自杀的能力，并描述这些概念的变化 自杀或自杀企图。 3。确定绝望，联系，心理痛苦和能力的预测有效性 在VA目前的两个预测模型中，有关退伍军人自杀企图和死亡率的自杀 在临床实践中的用途：风暴和覆盖范围。 方法论：拟议的混合方法研究具有探索性顺序设计，其中是定性的 组件（AIM 1）通知定量分析（目标2和3）。数据收集将来自现有的临床 VHA公司数据仓库，VA的自杀预防应用网络中的进度注释以及 VA/DOD自杀数据存储库。我们将使用语言注释和主题分析作为目标1和 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获得护理的人。 下一步/实施：我们最重要的下一步是与地方和国家定期联系 弗吉尼亚州心理健康和自杀预防办公室（OMHSP）的同事，以促进实施 我们在风暴和Reachvet的操作版本中的结果。