Prediction of psychosis across protocols and risk cohorts using automated language analysis
Prediction of psychosis across protocols and risk cohorts using automated language analysis
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DOI：
10.1002/wps.20491
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发表时间：
2018-02-01
期刊：
影响因子：
73.3
通讯作者：
Cecchi, Guillermo A.
中科院分区：
文献类型：
--
作者：
Corcoran, Cheryl M.;Carrillo, Facundo;Cecchi, Guillermo A.
语言和言语是精神科医生诊断和治疗精神障碍的主要数据来源。在精神病中，语言的结构本身可能会受到干扰，包括语义连贯性（例如，脱轨和相切性）和语法复杂性（例如，具体性）。在精神分裂症中，语言的细微障碍是明显的，甚至在第一次精神病发作之前，在前驱期。使用基于计算机的自然语言处理分析，我们以前表明，在讲英语的临床（例如，超）高危青年，语义连贯性（言语中的意义流动）和句法复杂性的基线降低可以高准确性地预测随后的精神病发作。在此，我们的目的是在第二个更大的风险队列中交叉验证这些自动语言分析方法，也是讲英语的，并区分精神病患者的语言与正常语言。我们确定了一种自动机器学习语音分类器-包括语义连贯性降低，连贯性差异更大，所有格代词的使用减少-在预测精神病发作方面具有83%的准确性（方案内），在原始风险队列中，交叉验证的精神病发作预测准确率为79%（跨协议），和72%的准确性，在区分最近发作的精神病患者的讲话，从健康人。该分类器与先前确定的手动语言预测高度相关。我们的研究结果支持自动化自然语言处理方法的实用性和有效性，以表征各阶段精神障碍的语义和句法障碍。下一步将是在更大的风险队列中应用这些方法，以进一步测试再现性，也包括英语以外的语言，并确定变异性的来源。这项技术有可能提高对高危青少年精神病结果的预测，并确定补救和预防干预的语言目标。更广泛地说，自动语言分析可以成为神经精神病学诊断和治疗的强大工具。
Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e.g., ultra) high-risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross-validate these automated linguistic analytic methods in a second larger risk cohort, also English-speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine-learning speech classifier - comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns - that had an 83% accuracy in predicting psychosis onset (intra-protocol), a cross-validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross-protocol), and a 72% accuracy in discriminating the speech of recent-onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at-risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry.