Identification of Trauma-related Features in EHR Data for Patients with Psychosis and Mood Disorders

精神病和情绪障碍患者 EHR 数据中创伤相关特征的识别

• 批准号: 10296954
• 负责人: Mei-Hua Hall
• 金额: $ 22.06万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10296954
• 关键词: Achievement; Adult; Architecture; Brain; Child Abuse; Child Sexual Abuse; Clinical; Clinical Trials; Collaborations; Communities; Complex; Data; Data Element; Databases; Development; Diagnostic; Dimensions; Electronic Health Record; Event; Exposure to; Female; Frequencies; Future; Gold; Guidelines; Health Care Costs; Heterogeneity; Hospitals; Human; Individual; Institution; Joints; Knowledge; Label; Life; Link; Machine Learning; Manuals; Massachusetts; Mediation; Mental disorders; Methods; Modeling; Mood Disorders; Names; Natural Language Processing; Outcome; Patients; Phenotype; Population; Prevalence; Psychopathology; Psychoses; Psychotic Disorders; Psychotic Mood Disorders; Recording of previous events; Relapse; Research; Research Domain Criteria; Research Personnel; Resistance; Resources; Risk; Societies; Source; Standardization; Subgroup; Substance abuse problem; Suicide; Supervision; Symptoms; Testing; Therapeutic Intervention; Time; Training; Trauma; Treatment outcome; Validation; base; clinical heterogeneity; clinical investigation; clinically significant; computing resources; cost effective; data modeling; data registry; data reuse; data standards; design; disability; emotional abuse; experience; hospital readmission; insight; machine learning method; male; natural language; new therapeutic target; patient health information; patient stratification; pediatric trauma; personalized medicine; physical abuse; repository; severe psychiatric disorder; structured data; therapy resistant; tool; treatment planning; unstructured data

Project Summary Psychotic and mood disorders represent a major driver of disability as well as health care cost. There is considerable clinical heterogeneity among patients. Developing clinically implementable machine learning (ML) tools to enable accurate patient stratification is critically important in order to augment effective personalized treatment plans. Among the factors contributing to heterogeneity, childhood trauma is an under-recognized source. The prevalence of childhood trauma is significant in adults with psychiatric disorders. Robust evidence shows that: i) individuals exposed to childhood abuse are 2-3 times more likely to develop a psychiatric disorder later in life, particularly psychosis; ii) childhood traumas impact critical windows of brain development and can trigger the onset of psychosis; and iii) among patients with psychotic and mood disorders, childhood trauma influences psychopathology, leading to more severe symptoms, poorer long-term outcomes (longer and higher rate of relapses or rehospitalization), associated with substance abuse, and are often treatment resistant and function poorly in society. Although evidence clearly indicates that childhood trauma contributes to psychiatric risk and poor treatment outcomes, large-scale computational approaches to stratify subpopulations, extract trauma features (e.g., frequency, type), and examine the links or the impact of trauma features on psychopathology and treatment outcome have yet to be developed. We propose to create gold standard annotations from Electronic health records (EHRs) and to leverage natural language processing (NLP) and ML methods to develop a standardized re-useable data model for automatically extracting trauma-related features, complex concepts, and symptom dimensions from EHRs. We will train and evaluate a semi-supervised NLP model, which is built as a joint sequence model that can both identify named entities as well as extract the relations between them. We will apply multiple strategies to validate the robustness of our model. Our proposed NLP model is essentially a “computational version of a chart review” tool, designed to mimic human chart review but performed automatically with the ability to scale. We will use this model to stratify psychosis subgroups (with or without childhood trauma history) and to correlate among the extracted features with important clinical outcome variables. Importantly, the annotation guidelines, corpus, and the data model developed by us will be valuable resources to researchers in the field. The study builds on existing collaborations between a team experienced in psychiatric phenotyping and application of EHRs, and a team active in developing and applying emerging methods in ML to natural language data. The model architecture developed in this application will lay the groundwork for a future clinical trial application.

项目摘要 精神病和情绪障碍代表了残疾和医疗保健费用的主要驱动力。有 患者之间的临床异质性很大。开发可实施的机器学习（ML） 为了增强有效的个性化，以实现准确的患者分层的工具至关重要 治疗计划。在导致异质性的因素中，童年创伤是一种不认可的 来源。儿童期创伤的患病率在精神疾病的成年人中很重要。强大的证据 表明：i）受到儿童虐待的人患有精神障碍的可能性高2-3倍 在生活中，尤其是精神病； ii）儿童创伤会影响大脑发育的关键窗户，并且可以 触发精神病的发作； iii）在精神病和情绪障碍的患者中，儿童创伤 影响心理病理学，导致更严重的症状，长期结局较差（越来越高） 继电器速率或重新住院的速率），与药物滥用相关，通常是耐药性和 尽管证据清楚地表明，童年创伤有助于精神病学 风险和不良治疗结果，大规模计算方法来分层亚群，提取 创伤特征（例如频率，类型），并检查创伤特征的链接或影响 精神病理学和治疗结果尚未开发。我们建议创建黄金标准 电子健康记录（EHR）的注释并利用自然语言处理（NLP）和ML 开发标准化可重复使用的数据模型的方法，用于自动提取与创伤相关的功能， EHR的复杂概念和症状维度。我们将训练和评估半监督的NLP 模型，该模型是作为一个联合序列模型构建的，既可以识别指定的实体，又可以提取 他们之间的关系。我们将采用多种策略来验证模型的鲁棒性。我们提出的 NLP模型本质上是“图表审查的计算版本”工具，旨在模仿人类图表审查 但以扩展能力自动执行。我们将使用此模型对精神病亚组进行分层（与 或没有童年创伤病史）并在提取的特征之间与重要的临床相关 结果变量。重要的是，我们开发的注释指南，语料库和数据模型将是 对该领域的研究人员有价值的资源。该研究以团队之间的现有合作为基础 在EHR的精神病表型和应用中经验丰富，以及一个积极开发和应用的团队 ML的新兴方法与自然语言数据。本应用程序中开发的模型架构将是 未来临床试验申请的基础。