心理学
精神病
精神分裂症(面向对象编程)
召回
名词
认知心理学
发展心理学
感觉
临床心理学
人工智能
精神科
计算机科学
社会心理学
作者
Ido Ziv,Heli Baram,Kfir Bar,Vered Zilberstein,Shmuel Itzikobitz,Eran Harel,Nachum Dershowitz
摘要
Psychosis is diagnosed based on disruptions in the structure and use of language, including reduced syntactic complexity, derailment, and tangentiality. With the development of computational analysis, natural language processing (NLP) techniques are used in many areas of life to make evaluations and inferences regarding people's thoughts, feelings and behavior. The present study explores morphological characteristic of schizophrenia inpatients using NLP. Transcripts of recorded stories by 49 male subjects (24 inpatients diagnosed with schizophrenia and 25 controls) about 14 Thematic Apperception Test (TAT) pictures were morphologically analyzed. Relative to the control group, the schizophrenic inpatients employed: (1) a similar ratio of nouns, but fewer verbs, adjectives and adverbs; (2) a higher ratio of lemmas to token (LTR) and type to token (TTR); (3) a smaller gap between LTR and TTR; and (4) greater use of the first person. The results were cross-verified using three well-known fitting classifier algorithms (Random Forest, XGBoost and a support vector machine). Tests of prediction accuracy, precision and recall found correct attribution of patients to the schizophrenia group at a rate of between 80 and 90%. Overall, the results suggest that the language of schizophrenic inpatients is significantly different from that of healthy controls, being morphologically less complex, more associative and more focused on the self. The findings support NLP analysis as a complementary addition to the traditional clinical psychosis evaluation for schizophrenia.
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