Mental Illness Research, Education, and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Schizophr Bull. 2023 Mar 15;49(2):444-453. doi: 10.1093/schbul/sbac126.
Disturbances in self-experience are a central feature of schizophrenia and its study can enhance phenomenological understanding and inform mechanisms underlying clinical symptoms. Self-experience involves the sense of self-presence, of being the subject of one's own experiences and agent of one's own actions, and of being distinct from others. Self-experience is traditionally assessed by manual rating of interviews; however, natural language processing (NLP) offers automated approach that can augment manual ratings by rapid and reliable analysis of text.
We elicited autobiographical narratives from 167 patients with schizophrenia or schizoaffective disorder (SZ) and 90 healthy controls (HC), amounting to 490 000 words and 26 000 sentences. We used NLP techniques to examine transcripts for language related to self-experience, machine learning to validate group differences in language, and canonical correlation analysis to examine the relationship between language and symptoms.
Topics related to self-experience and agency emerged as significantly more expressed in SZ than HC (P < 10-13) and were decoupled from similarly emerging features such as emotional tone, semantic coherence, and concepts related to burden. Further validation on hold-out data showed that a classifier trained on these features achieved patient-control discrimination with AUC = 0.80 (P < 10-5). Canonical correlation analysis revealed significant relationships between self-experience and agency language features and clinical symptoms.
Notably, the self-experience and agency topics emerged without any explicit probing by the interviewer and can be algorithmically detected even though they involve higher-order metacognitive processes. These findings illustrate the utility of NLP methods to examine phenomenological aspects of schizophrenia.
自我体验障碍是精神分裂症的一个核心特征，对其研究可以增强对现象学的理解，并为临床症状的潜在机制提供信息。自我体验涉及自我存在感、作为自身体验的主体以及自身行为的执行者，以及与他人的区别。自我体验传统上通过访谈的手动评分来评估；然而，自然语言处理（NLP）提供了一种自动化的方法，可以通过快速可靠地分析文本来增强手动评分。
我们从 167 名精神分裂症或分裂情感障碍（SZ）患者和 90 名健康对照（HC）中引出自传体叙述，共计 49 万单词和 26000 个句子。我们使用 NLP 技术检查转录本中与自我体验相关的语言，使用机器学习验证语言在组间的差异，并使用典型相关分析检查语言与症状之间的关系。
与自我体验和能动性相关的主题在 SZ 中比 HC 更明显地表达（P < 10-13），并且与类似出现的特征（如情绪基调、语义连贯性和与负担相关的概念）脱钩。在保留数据上的进一步验证表明，基于这些特征训练的分类器在患者与对照组的区分中达到 AUC = 0.80（P < 10-5）。典型相关分析显示自我体验和能动性语言特征与临床症状之间存在显著的关系。
值得注意的是，这些自我体验和能动性主题在访谈者没有任何明确探查的情况下出现，并且即使涉及到更高阶的元认知过程，也可以通过算法检测到。这些发现说明了 NLP 方法在检查精神分裂症现象学方面的效用。
