Automated linguistic analyses of semantics and syntax in speech output in the psychosis prodrome: A novel paradigm to evaluate subtle thought disorder.

精神病前驱症状中语音输出的语义和句法的自动语言分析：评估微妙思维障碍的新范式。

• 批准号: 9558919
• 负责人: CHERYL MARY CORCORAN
• 金额: $ 4.77万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-25 至 2019-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9558919
• 关键词: Automated; linguistic; analyses; semantics; syntax; Automated; linguistic; analyses; semantics; syntax

 DESCRIPTION (provided by applicant): In an effort to intervene before psychosis onset and prevent morbidity, a major recent focus in schizophrenia research has been the identification of young people during a putative prodromal period, so as to develop safe and effective interventions to modify disease course. Over the past decades, studies at Columbia and elsewhere have evaluated clinical high-risk (CHR) individuals across a range of cognitive processes in an effort to identify core deficits of schizophrenia evident before psychosis onset. Subtle thought disorder, manifest in disturbance of language production, is a feature that predates rather than follows, psychosis onset in CHR individuals, and therefore may be an indicator of schizophrenia liability. Subtle thought disorder in schizophrenia and its risk states has typically been evaluated using clinical rating scales, and occasionally labor-intensive manual methods of linguistic analysis. Here, we propose to instead use a novel automated machine-learning approach to speech analysis informed by artificial intelligence. The method derives the semantic meaning of words and phrases by drawing on a large corpus of text, similar to how humans assign meaning to language. It also evaluates syntax through "part-of-speech" tagging. These analyses yield fine-grained indices of speech semantics and syntax that may more accurately capture subtle thought disorder and discriminate psychosis outcome among CHR individuals. Using these automated methods of speech analysis, in collaboration with computer scientists from IBM, we were able to identify a classifier with high accuracy for psychosis onset in a small CHR cohort at Columbia, which included semantic coherence from phrase to phrase, shortened phrase length, and decreased use of determiner pronouns ("which", "what", "that"). These features were correlated with prodromal symptoms but outperformed them in terms of classification accuracy. They also discriminated schizophrenia from normal speech. While promising, these automated methods of analysis require validation in a second CHR cohort. In this proposal, in collaboration with IBM, we will validate these automated methods using a large archive of speech data from the UCLA CHR cohort. This dataset has several advantages. First, the UCLA CHR cohort has a high prevalence of psychosis transition, important as machine learning is sensitive to group size. Second, it has undergone prior manual linguistic analysis, identifying features of language production that predicted psychosis outcome; hence, automated and manual methods can be directly compared. Third, there are speech data available from healthy controls and recent-onset psychosis patients (for validation). Fourth, several participants have multiple speech assays (such that stability of the classifier can be examined). Beyond validation of methods, we will maximize group size and combine speech data from Columbia and UCLA to characterize a common classifier of psychosis outcome. Automated methods for language analysis may improve prediction of psychosis onset and inform remediation strategies for its prevention.

 描述（由适用提供）：为了在精神病发作前进行干预并预防发病率，精神分裂症研究的主要重点是在假定的前驱时期对年轻人的鉴定，以开发安全有效的干预措施以修改疾病病程。在过去的几十年中，哥伦比亚和其他地方的研究评估了一系列认知过程中的临床高危（CHR）个体，以识别精神分裂症开始前精神分裂症证据的核心定义。微妙的思想障碍体现在语言生产障碍中，是一个呈现的特征，而不是遵循CHR个人的精神病发作，因此可能是精神分裂症责任的指标。精神分裂症及其风险状态的微妙思想障碍通常使用临床评级量表进行评估，偶尔进行了实验室密集型语言分析方法。在这里，我们建议使用一种新型的自动化机器学习方法来通过人工智能告知的语音分析。该方法通过绘制大量文本语料库来得出单词和短语的语义含义，类似于人类对语言的含义的分配方式。它还通过“语音”标签评估语法。这些分析产生了语音语义和语法的细粒指数，这些指标可能更准确地捕获微妙的思想障碍并区分CHR个体的精神病结果。使用这些自动化的语音分析方法，与IBM的计算机科学家合作，我们能够在哥伦比亚的一个小CHR群中确定具有高准确性精神病的分类器，其中包括从短语到短语到短语长度的语义连贯性，短语长度，以及对确定代码的使用量的使用，并增加了确定代码的使用。这些特征与前驱症状相关，但在分类准确性方面表现优于它们。他们还将精神分裂症与正常言语区分开。在承诺的同时，这些自动化的分析方法需要在第二个CHR群体中进行验证。在此提案中，与IBM合作，我们将使用来自UCLA CHRORT的大量语音数据验证这些自动化方法。该数据集具有几个优点。首先，加州大学洛杉矶分校CHR群体的精神病过渡率很高，因为机器学习对群体大小敏感。其次，它进行了先前的手动语言分析，确定了预测精神病结果的语言生产特征。因此，可以直接比较自动化和手动方法。第三，有来自健康控制和最近发作的精神病患者（用于验证）的语音数据。第四，一些参与者有多次语音评估（可以检查分类器的稳定性）。除了验证方法之外，我们还将最大程度地提高群体规模，并结合哥伦比亚和UCLA的语音数据，以表征精神病结果的共同分类器。语言分析的自动化方法可以改善精神病的预测，并为预防措施提供补救策略。