Natural language processing for characterizing psychopathology

用于表征精神病理学的自然语言处理

基本信息

批准号：
9254614
负责人：
ROY H. Perlis
金额：
$ 37.73万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-04-06 至 2019-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9254614
关键词：
Admission activity Antidepressive Agents Applaud Area Back Biology Categories Clinical Clinical Data Clinical stratification Code Complex DSM-IV Data Data Set Development Diagnosis Diagnostic Dimensions Disease Electronic Health Record Epidemiology Face Genetic Health Health behavior Health system Healthcare Systems Hospitals Individual Inpatients Intervention Interview Investigation Length of Stay Machine Learning Measures Medical Mental Depression Mental disorders Methodology Methods Modeling Moods National Institute of Mental Health Natural Language Processing New England Outcome Patient Outcomes Assessments Patients Penetration Pharmaceutical Preparations Psychiatric Diagnosis Psychiatry Psychopathology Public Health Registries Reporting Research Domain Criteria Research Personnel Resources Risk Risk stratification Severities Structure Subgroup Symptoms System Text United States National Academy of Sciences Work base clinical investigation clinically relevant cohort cost health data health record hospital readmission improved natural language neuropsychiatric symptom novel outcome prediction precision medicine predict clinical outcome public health relevance skills stressor success terabyte tool translational scientist trend

项目摘要

DESCRIPTION (provided by applicant): Convergent genetic and epidemiologic evidence suggests the importance of understanding psychiatric illness from a dimensional rather than solely a categorical perspective. The limitations of traditional diagnostic categories motivated a major NIMH-supported effort to identify measures of psychopathology that more closely align with underlying disease biology. At present, however, the available large clinical data sets, whether health claims, registries, or electronic health records, do not include such dimensional measures. Even with the integration of structure clinician and patient-reported outcomes, generating such cohorts could require a decade or more. Moreover, coded data does not systematically capture clinically-important concepts such as health behaviors or stressors. While such cohorts are developed, natural language processing can facilitate the application of existing electronic health records to enable precision medicine in psychiatry. Specifically, while traditional natural language tools focus on extracting individual terms, emerging methods including those in development by the investigators allow extraction of concepts and dimensions. The present investigation proposes to develop a toolkit for natural language processing of narrative patient notes to extract measures of psychopathology, including estimated RDoC domains. In preliminary investigations in a large health system, these tools have demonstrated both face validity and predictive validity. This toolkit also allows extraction o complex concepts from narrative notes, such as stressors and health behaviors. In the proposed study, these natural language processing tools will be applied to a large psychiatric inpatient data set as well as a large general medical inpatient data set, to derive measures of psychopathology and other topics. The resulting measures will then be used in combination with coded data to build regression and machine-learning-based models to predict clinical outcomes including length of hospital stay and risk of readmission. The models will then be validated in independent clinical cohorts. By combining expertise in longitudinal clinical investigation, natural language processing, and machine learning, the proposed study brings together a team with the needed skills to develop a critical toolkit for understanding health records dimensionally The resulting models can be applied to facilitate investigation of dimensions of psychopathology and related topics, allowing stratification of clinical risk to enable development of targeted interventions.

描述（由申请人提供）：融合的遗传和流行病学证据表明，从维度而不是仅仅从分类角度理解精神疾病的重要性传统诊断的局限性促使 NIMH 支持的一项重大工作是确定更紧密分类的精神病理学测量方法。然而，目前可用的大型临床数据集，无论是健康声明、登记还是电子健康记录，都不包括此类维度的衡量标准。此外，编码数据无法系统地捕获健康行为或压力源等临床重要概念，但自然语言处理可以促进现有数据的应用。具体来说，传统的自然语言工具侧重于提取单个术语，而包括研究人员正在开发的方法在内的新兴方法允许提取概念和维度。处理叙述性患者笔记提取精神病理学的测量值，包括估计的 RDoC 领域。在大型卫生系统的初步调查中，这些工具已经证明了表面有效性和预测有效性。该工具包还可以从叙述笔记中提取复杂的概念，例如压力源和健康行为。在拟议的研究中，这些自然语言处理工具将应用于大型精神病住院患者数据集以及大型普通医疗住院患者数据集，以得出精神病理学和其他主题的测量结果，然后将所得测量结果与编码结合使用。建立回归的数据和然后，通过结合纵向临床调查、自然语言处理和机器学习方面的专业知识，这些模型将在独立的临床队列中得到验证。汇集了一支具有所需技能的团队，开发了一个关键工具包，用于从维度上理解健康记录。由此产生的模型可用于促进精神病理学和相关主题维度的调查，从而对临床风险进行分层，从而能够制定有针对性的干预措施。