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

 描述（由申请人提供）：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近期焦点是在推定的前驱期识别年轻人，以便开发安全有效的干预措施来改变病程。在过去的几十年里，哥伦比亚和其他地方的研究已经评估了临床高风险个体的一系列认知过程，以确定精神分裂症发作前明显的核心缺陷。微妙的思维障碍，表现在语言产生障碍，是一个功能，早于，而不是遵循，精神病发作在精神分裂症的个人，因此可能是精神分裂症的一个指标。精神分裂症中的微妙思维障碍及其风险状态通常使用临床评级量表进行评估，偶尔使用劳动密集型的语言分析手工方法。在这里，我们建议使用一种新的自动机器学习方法来进行人工智能告知的语音分析。该方法通过利用大量文本语料库来推导单词和短语的语义，类似于人类如何为语言赋予意义。它还通过“词性”标记来评估句法。这些分析产生细粒度的语音语义和句法的指数，可以更准确地捕捉微妙的思维障碍和歧视的精神病患者之间的结果。使用这些语音分析的自动化方法，与IBM的计算机科学家合作，我们能够在哥伦比亚的一个小型队列中识别出一个高准确度的精神病发作分类器，其中包括短语之间的语义连贯性，缩短短语长度，减少使用限定代词（“which”，“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。他们还将精神分裂症与正常言语区分开来。虽然很有前途，但这些自动化分析方法需要在第二个队列中进行验证。在这项提案中，我们将与IBM合作，使用来自UCLA的语音数据库来验证这些自动化方法。这个数据集有几个优点。首先，加州大学洛杉矶分校的精神病患者群体的精神病转变患病率很高，这一点很重要，因为机器学习对群体规模很敏感。其次，它经历了先前的手动语言分析，识别预测精神病结果的语言产生特征;因此，可以直接比较自动和手动方法。第三，有来自健康对照组和近期发作的精神病患者的语音数据（用于验证）。第四，若干参与者具有多个语音测定（使得可以检查分类器的稳定性）。除了方法的验证，我们将最大限度地扩大组的规模，并结合联合收割机的语音数据从哥伦比亚和加州大学洛杉矶分校的特点，一个共同的分类精神病的结果。语言分析的自动化方法可以改善对精神病发作的预测，并为预防提供补救策略。

 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.