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

项目摘要 精神病和情绪障碍是导致残疾的主要原因，也是医疗费用的主要因素。的确有 患者之间存在相当大的临床异质性。开发临床可实现的机器学习(ML) 实现准确的患者分层的工具对于增强有效的个性化至关重要 治疗计划。在导致异质性的因素中，童年创伤是一个被低估的因素。 消息来源。在患有精神障碍的成年人中，童年创伤的患病率很高。有力的证据 表明：i)遭受儿童期虐待的人患精神障碍的可能性是前者的2-3倍 晚年，特别是精神病；ii)童年创伤影响大脑发育的关键窗口，并可能 触发精神病的发病；以及iii)在患有精神病和情绪障碍的患者中，童年创伤 影响精神病理学，导致更严重的症状，更差的长期结果(更长和更高 复发率或再住院率)，与药物滥用有关，通常具有治疗耐药性和 在社会中的作用很差。尽管有证据清楚地表明，童年创伤会导致精神疾病 风险和不良治疗结果，分层的大规模计算方法，摘录 创伤特征(例如，频率、类型)，并检查创伤特征对 精神病理学和治疗结果还有待开发。我们建议创建金本位制 来自电子健康记录(EHR)的注释，并利用自然语言处理(NLP)和ML 方法开发标准化的可重复使用的数据模型以自动提取创伤相关特征， 来自EHR的复杂概念和症状维度。我们将训练和评估一个半监督的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健壮性。我们的建议 NLP模型本质上是一个“计算版本的图表评审”工具，旨在模仿人类的图表评审 但可以自动执行，并具有扩展能力。我们将使用这个模型来对精神病亚群进行分层(与 或没有童年创伤病史)，并将提取的特征与重要的临床相关 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 为该领域的研究人员提供宝贵的资源。这项研究建立在一个团队之间现有的合作基础上 在精神病学表型和EHR的应用方面经验丰富，并拥有一支积极开发和应用的团队 自然语言数据ML中的新兴方法。在此应用程序中开发的模型体系结构将 为将来的临床试验应用奠定了基础。

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.