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

 描述(申请人提供)：为了在精神病发作前进行干预并预防发病，精神分裂症研究的一个主要焦点是在假定的先兆时期识别年轻人，以便开发安全和有效的干预措施来改变病程。在过去的几十年里，哥伦比亚大学和其他地方的研究对临床高危(CHR)个体进行了一系列认知过程的评估，以努力识别精神分裂症在精神病发作之前明显的核心缺陷。微妙的思维障碍，表现为语言产生的障碍，是一种特征，在精神分裂症发作之前而不是之后出现，因此可能是精神分裂症易感性的一个指标。精神分裂症及其风险状态中的微妙思维障碍通常使用临床评分表进行评估，偶尔还会使用劳动密集型的人工语言分析方法进行评估。在这里，我们建议改用一种新的自动机器学习方法来进行人工智能通知的语音分析。该方法通过利用大型文本语料库来推导单词和短语的语义，类似于人类赋予语言意义的方式。它还通过“词性”标记来评估句法。这些分析产生了语音、语义和句法的细粒度索引，可能更准确地捕捉到微妙的思维障碍，并区分CHR个体中的精神病结果。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大学的一个小CHR队列中识别出对精神病发作具有高精度的分类器，其中包括短语之间的语义一致性、缩短的短语长度以及减少使用限定代词(“What”、“What”、“That”)。这些特征与前驱症状相关，但在分类准确性方面优于它们。他们还区分了精神分裂症和正常语言。尽管前景看好，但这些自动化的分析方法需要在第二个CHR队列中进行验证。在这份提案中，我们将与IBM合作，使用加州大学洛杉矶分校的CHR队列中的大量语音数据来验证这些自动化方法。此数据集有几个优点。首先，加州大学洛杉矶分校的CHR队列有很高的精神病转变发生率，这一点很重要，因为机器学习对群体规模很敏感。其次，它经过了先前的人工语言分析，确定了预测精神病结果的语言产生的特征；因此，自动方法和手动方法可以直接进行比较。第三，有来自健康对照和新近发病的精神病患者的语音数据(用于验证)。第四，几个参与者有多个语音分析(以便可以检查分类器的稳定性)。除了方法的验证之外，我们还将最大限度地扩大群体规模，并结合哥伦比亚大学和加州大学洛杉矶分校的语音数据来表征精神病结果的常见分类器。自动化的语言分析方法可以改善对精神病发病的预测，并为预防精神病的补救策略提供信息。

 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.