Natural language processing for characterizing psychopathology

用于表征精神病理学的自然语言处理

• 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
• 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

 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。传统诊断类别的局限性促使 NIMH 支持一项重大努力，以确定与潜在疾病生物学更密切相关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，都不包括此类维度测量。即使整合了临床医生和患者报告的结果，生成这样的队列也可能需要十年或更长时间。此外，编码数据不能系统地捕获临床上重要的概念，例如健康行为或压力源。 在开发此类队列的同时，自然语言处理可以促进现有电子健康记录的应用，从而实现精神病学中的精准医疗。具体来说，虽然传统的自然语言工具侧重于提取单个术语，但新兴方法（包括研究人员正在开发的方法）允许提取概念和维度。 目前的调查建议开发一个工具包，用于对叙述性患者笔记进行自然语言处理，以提取精神病理学的测量值，包括估计的 RDoC 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。该工具包还允许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内科住院患者数据集，以得出精神病理学和其他主题的测量结果。然后，所得测量结果将与编码数据结合使用，构建回归和基于机器学习的模型，以预测临床结果，包括住院时间和再入院风险。然后，这些模型将在独立的临床队列中得到验证。 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，拟议的研究汇集了一个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。所得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。