Automated analysis of free speech predicts psychosis onset in high-risk youths
Automated analysis of free speech predicts psychosis onset in high-risk youths
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DOI：
10.1038/npjschz.2015.30
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发表时间：
2015-01-01
影响因子：
5.4
通讯作者：
Corcoran, Cheryl M.
中科院分区：
文献类型：
--
作者：
Bedi, Gillinder;Carrillo, Facundo;Corcoran, Cheryl M.
背景/说明：精神病学缺乏其他专业常规使用的客观临床测试。新的计算机化方法来表征复杂的行为，如语音，可用于识别和预测精神疾病的individual.AIMS：在这项证明的原则研究，我们的目的是测试自动语音分析结合机器学习，以预测后期精神病发作的青年在临床高风险（ESTA）的精神病。方法：34名青少年（11名女性）进行了基线访谈，并在长达2.5年的时间内每季度进行评估; 5名转变为精神病。使用自动化分析，访谈的成绩单进行了评估的语义和句法特征预测以后的精神病发作。语音特征被送入一个凸船体分类算法与留一主题交叉验证，以评估其预测价值的精神病的结果。语音特征和前驱症状rates.Results之间的典型相关性计算：派生的语音特征包括潜在语义分析的语义连贯性和语音复杂性的两个句法标记：最大短语长度和限定词的使用（例如，其中）。这些语音特征预测后期精神病发展的准确率为100%，优于临床访谈的分类。语音功能与前驱症状显着相关。结论：研究结果支持自动语音分析的效用，以衡量微妙的，临床相关的精神状态变化，在紧急精神病。计算机科学的最新发展，包括自然语言处理，可以为精神病学客观临床测试的未来发展提供基础。
BACKGROUND/OBJECTIVES: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals.AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis.METHODS: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed.RESULTS: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms.CONCLUSIONS: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry.