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: 10427433
• 负责人: Mei-Hua Hall
• 金额: $ 24.54万
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10427433
• 关键词: Achievement; Adult; Architecture; Brain; Child Abuse; Child Sexual Abuse; Clinical; Clinical Trials; Collaborations; Communities; Complex; Data; Data Element; Databases; Development; 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; diagnostic strategy; disability; emotional abuse; experience; hospital readmission; insight; machine learning method; machine learning model; 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.

项目概要 精神病和情绪障碍是残疾和医疗保健费用的主要驱动因素。有 患者之间存在相当大的临床异质性。开发临床上可实施的机器学习 (ML) 为了增强有效的个性化治疗，实现准确患者分层的工具至关重要 治疗计划。在造成异质性的因素中，童年创伤是一个未被充分认识的因素。 来源。在患有精神疾病的成年人中，童年创伤的患病率很高。有力的证据 表明： i) 童年时期遭受虐待的人患精神疾病的可能性是其他人的 2-3 倍 晚年，尤其是精神病； ii) 童年创伤影响大脑发育的关键窗口并可能 引发精神病发作； iii) 患有精神病和情绪障碍、童年创伤的患者 影响精神病理学，导致更严重的症状、更差的长期结果（更长、更高 复发或再住院率），与药物滥用有关，并且通常对治疗有抵抗力 在社会中运作不佳。尽管有证据明确表明童年创伤会导致精神疾病 风险和不良治疗结果，大规模计算方法对亚群进行分层，提取 创伤特征（例如频率、类型），并检查创伤特征对患者的联系或影响 精神病理学和治疗结果仍有待开发。我们建议创建黄金标准 来自电子健康记录 (EHR) 的注释并利用自然语言处理 (NLP) 和 ML 开发标准化可重用数据模型以自动提取创伤相关特征的方法， 电子病历中的复杂概念和症状维度。我们将训练和评估半监督 NLP 模型，它被构建为联合序列模型，既可以识别命名实体，也可以提取 他们之间的关系。我们将应用多种策略来验证我们模型的稳健性。我们提出的 NLP 模型本质上是一个“图表审查的计算版本”工具，旨在模仿人类图表审查 但可以自动执行并具有扩展能力。我们将使用这个模型对精神病亚组进行分层（ 或没有童年创伤史）并将提取的特征与重要的临床相关联 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 为该领域的研究人员提供宝贵的资源。该研究建立在团队之间现有合作的基础上 在精神科表型分析和电子病历应用方面经验丰富，并拥有一支积极开发和应用的团队 机器学习中自然语言数据的新兴方法。本应用中开发的模型架构将奠定 为未来的临床试验应用奠定基础。