Just Sandra Anna, Bröcker Anna-Lena, Ryazanskaya Galina, Nenchev Ivan, Schneider Maria, Bermpohl Felix, Heinz Andreas, Montag Christiane
Department of Psychiatry and Neurosciences, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
Department of Linguistics, University of Potsdam, Potsdam, Germany.
Front Psychiatry. 2023 Jul 25;14:1208856. doi: 10.3389/fpsyt.2023.1208856. eCollection 2023.
Impairments in speech production are a core symptom of non-affective psychosis (NAP). While traditional clinical ratings of patients' speech involve a subjective human factor, modern methods of natural language processing (NLP) promise an automatic and objective way of analyzing patients' speech. This study aimed to validate NLP methods for analyzing speech production in NAP patients.
Speech samples from patients with a diagnosis of schizophrenia or schizoaffective disorder were obtained at two measurement points, 6 months apart. Out of = 71 patients at T, speech samples were also available for = 54 patients at T. Global and local models of semantic coherence as well as different word embeddings (word2vec vs. GloVe) were applied to the transcribed speech samples. They were tested and compared regarding their correlation with clinical ratings and external criteria from cross-sectional and longitudinal measurements.
Results did not show differences for global vs. local coherence models and found more significant correlations between word2vec models and clinically relevant outcome variables than for GloVe models. Exploratory analysis of longitudinal data did not yield significant correlation with coherence scores.
These results indicate that natural language processing methods need to be critically validated in more studies and carefully selected before clinical application.
言语产生障碍是非情感性精神病（NAP）的核心症状。虽然对患者言语的传统临床评分涉及人为主观因素，但现代自然语言处理（NLP）方法有望提供一种自动且客观的方式来分析患者的言语。本研究旨在验证用于分析NAP患者言语产生的NLP方法。
从诊断为精神分裂症或分裂情感性障碍的患者中获取言语样本，在两个相隔6个月的测量点进行采集。在T1时的71名患者中，有54名患者在T2时也有言语样本。将语义连贯的全局和局部模型以及不同的词嵌入（word2vec与GloVe）应用于转录后的言语样本。针对它们与临床评分以及横断面和纵向测量的外部标准之间的相关性进行了测试和比较。
全局与局部连贯模型的结果未显示出差异，并且发现word2vec模型与临床相关结局变量之间的相关性比GloVe模型更显著。对纵向数据的探索性分析未得出与连贯分数的显著相关性。
这些结果表明，自然语言处理方法在临床应用前需要在更多研究中进行严格验证并谨慎选择。
