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：减少美国退伍军人的自杀和自杀企图是国家的主要优先事项，因为 每年有6000多名退伍军人自杀身亡，还有更多的人试图自杀。2017年，最新的 在有数据的年份，退伍军人的自杀率是非退伍军人的1.5倍， 退伍军人女性自杀率是非退伍军人自杀率的2.2倍。目前的VHA自杀 风险预测模型存在大量假阴性-退伍军人不被认为存在高风险 自杀未遂或自杀身亡的人。这些自杀预测模型没有将富人纳入其中 临床进展笔记中的信息可能会提高我们预测自杀行为的能力。其中很大一部分 临床进展笔记中的信息是非结构化的自由文本。特定于自杀的本体论和信息 可以从非结构化临床进展记录中提取与自杀有关的信息的提取系统不是 可用。 意义/影响：增强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，在他们 企图自杀或自杀而死。拟议的研究与高铁和高铁研究和发展的优先事项很好地一致 退伍军人管理局领导层制定了2018-2024年退伍军人管理局战略目标，并将自杀预防列为退伍军人管理局的 临床上最优先考虑的问题。 创新：我们的关键方法创新是配对最先进的理论框架(3步 自杀理论)用最先进的数据预测谁最有可能按照他们的自杀念头行事 科学方法(NLP、机器学习)。既然我们的自杀论概念，那就是绝望， 连接性、心理痛苦和自杀能力没有体现在结构化的患者数据中，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笔记，潜在的 其中还没有完全征收，以改善自杀预测模型。 具体目标：我们有三个具体目标： 1.开发特定于自杀的本体，用于机器识别无望、连通性、 心理痛苦和自杀能力进展中与退伍军人临床接触的笔记 自杀未遂或自杀身亡。 2.提取关于绝望、联系、心理痛苦的存在和强度的信息， 和自杀能力在临床进展记录中，并描述这些概念在接近 自杀或自杀未遂。 3.确定无望感、连通性、心理痛苦和能力的预测效度 退伍军人自杀未遂与死亡率的关系目前在两个预测模型中 临床应用：STORM和REACHVET。 方法：拟议的混合方法研究采用探索性顺序设计，其中定性 构成部分(目标1)提供定量分析信息(目标2和3)。数据收集将来自现有的临床 VHA公司数据仓库、退伍军人管理局自杀预防应用网络和 退伍军人事务部/国防部自杀数据库。我们将使用语言注释和主题分析来实现目标1和 目标2和目标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接受护理的人。 下一步/实施：我们最重要的下一步是与地方和国家保持定期联系 退伍军人事务部精神健康和自杀预防办公室的同事们，以促进 我们在STORM和REACHVET运行版本中的结果。