Huang Y.-JYI-TING LINCHEN-CHUNG LIULee L.-EHung S.-HLo J.-KLI-CHEN FU2023-06-092023-06-09202215344320https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127482030&doi=10.1109%2fTNSRE.2022.3163777&partnerID=40&md5=8897ebae43bf6f7cf6a078822ba76da6https://scholars.lib.ntu.edu.tw/handle/123456789/632420Thought, language, and communication disorders are among the salient characteristics of schizophrenia. Such impairments are often exhibited in patients' conversations. Researches have shown that assessments of thought disorder are crucial for tracking the clinical patients' conditions and early detection of clinical high-risks. Detecting such symptoms require a trained clinician's expertise, which is prohibitive due to cost and the high patient-to-clinician ratio. In this paper, we propose a machine learning method using Transformer-based model to help automate the assessment of the severity of the thought disorder of schizophrenia. The proposed model uses both textual and acoustic speech between occupational therapists or psychiatric nurses and schizophrenia patients to predict the level of their thought disorder. Experimental results show that the proposed model has the ability to closely predict the results of assessments for Schizophrenia patients base on the extracted semantic, syntactic and acoustic features. Thus, we believe our model can be a helpful tool to doctors when they are assessing schizophrenia patients. © 2001-2011 IEEE.human speech processing; natural language processing; negative symptoms; positive symptoms; Schizophrenia; thought disorder[SDGs]SDG3Deep learning; Diseases; Feature extraction; Job analysis; Learning algorithms; Natural language processing systems; Semantics; Speech processing; Syntactics; Acoustic features; Bit-error rate; Features extraction; Human speech processing; Interview; Negative symptom; Positive symptom; Schizophrenia patients; Task analysis; Thought disorder; Clinical research; accuracy; acoustics; adult; Article; artifact; automatic speech recognition; bert model; case report; Chinese; clinical article; clinical feature; communication disorder; comparative study; controlled study; deep learning; delusion; depression; disease severity assessment; electra model; feature learning (machine learning); female; human; language ability; language disability; long short term memory network; machine learning; male; middle aged; natural language processing; negative syndrome; Positive and Negative Syndrome Scale; positive syndrome; prediction; psychological interview; psychosis; schizophrenia; semantics; speech; speech and language assessment; speech test; SYNTAX score; tera model; thought disorder; verbal communication; word processing; acoustics; linguistics; schizophrenia; Acoustics; Deep Learning; Humans; Linguistics; Schizophrenia; Speechjournal article10.1109/TNSRE.2022.3163777353580492-s2.0-85127482030