Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques
Thought, 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’ con...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9745530/ |
| _version_ | 1797805172668235776 |
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| author | Yan-Jia Huang Yi-Ting Lin Chen-Chung Liu Lue-En Lee Shu-Hui Hung Jun-Kai Lo Li-Chen Fu |
| author_facet | Yan-Jia Huang Yi-Ting Lin Chen-Chung Liu Lue-En Lee Shu-Hui Hung Jun-Kai Lo Li-Chen Fu |
| author_sort | Yan-Jia Huang |
| collection | DOAJ |
| description | Thought, 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. |
| first_indexed | 2024-03-13T05:47:07Z |
| format | Article |
| id | doaj.art-a5ed7a95173f4c0680f6de54f1d7c092 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:47:07Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-a5ed7a95173f4c0680f6de54f1d7c0922023-06-13T20:08:16ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-013094795610.1109/TNSRE.2022.31637779745530Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning TechniquesYan-Jia Huang0https://orcid.org/0000-0003-3501-8916Yi-Ting Lin1https://orcid.org/0000-0003-3821-4744Chen-Chung Liu2https://orcid.org/0000-0002-9290-110XLue-En Lee3https://orcid.org/0000-0001-6751-9825Shu-Hui Hung4Jun-Kai Lo5https://orcid.org/0000-0002-1791-5763Li-Chen Fu6https://orcid.org/0000-0002-6947-7646Department of Computer Science and Information Engineering, National Taiwan University, Taipei, TaiwanDepartment of Psychiatry, National Taiwan University Hospital, Taipei, TaiwanDepartment of Psychiatry, National Taiwan University Hospital, Taipei, TaiwanDepartment of Psychiatry, National Taiwan University Hospital, Taipei, TaiwanNational Center for High-Performance Computing, Taichung, ChinaDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, TaiwanThought, 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.https://ieeexplore.ieee.org/document/9745530/Schizophreniathought disorderpositive symptomsnegative symptomsnatural language processinghuman speech processing |
| spellingShingle | Yan-Jia Huang Yi-Ting Lin Chen-Chung Liu Lue-En Lee Shu-Hui Hung Jun-Kai Lo Li-Chen Fu Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques IEEE Transactions on Neural Systems and Rehabilitation Engineering Schizophrenia thought disorder positive symptoms negative symptoms natural language processing human speech processing |
| title | Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques |
| title_full | Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques |
| title_fullStr | Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques |
| title_full_unstemmed | Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques |
| title_short | Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques |
| title_sort | assessing schizophrenia patients through linguistic and acoustic features using deep learning techniques |
| topic | Schizophrenia thought disorder positive symptoms negative symptoms natural language processing human speech processing |
| url | https://ieeexplore.ieee.org/document/9745530/ |
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