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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.