Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140127
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dc.contributor.authorXu, Shihaoen_US
dc.contributor.authorYang, Zixuen_US
dc.contributor.authorChakraborty, Debsubhraen_US
dc.contributor.authorTahir, Yasiren_US
dc.contributor.authorMaszczyk, Tomaszen_US
dc.contributor.authorChua, Victoria Yi Hanen_US
dc.contributor.authorDauwels, Justinen_US
dc.contributor.authorThalmann, Danielen_US
dc.contributor.authorThalmann, Nadia Magnenaten_US
dc.contributor.authorTan, Bhing-Leeten_US
dc.contributor.authorLee, Jimmy Chee Keongen_US
dc.date.accessioned2020-05-26T12:16:27Z-
dc.date.available2020-05-26T12:16:27Z-
dc.date.issued2019-
dc.identifier.citationXu, S., Yang, Z., Chakraborty, D., Tahir, Y., Maszczyk, T., Chua, V. Y. H., . . . Lee, J. C. K. (2019). Automatic verbal analysis of interviews with schizophrenic patients. Proceedings of the 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). doi:10.1109/ICDSP.2018.8631830en_US
dc.identifier.issn978-981-13-9443-0-
dc.identifier.urihttps://hdl.handle.net/10356/140127-
dc.description.abstractSchizophrenia is a long-term mental disease associated with language impairments that affect about one percent of the population. Traditional assessment of schizophrenic patients is conducted by trained professionals, which requires tremendous resources of time and effort. This study is part of a larger research objective committed to creating automated platforms to aid clinical diagnosis and understanding of schizophrenia. We have analyzed non-verbal cues and movement signals in our previous work. In this study, we explore the feasibility of using automatic transcriptions of interviews to classify patients and predict the observability of negative symptoms in schizophrenic patients. Interview recordings of 50 schizophrenia patients and 25 age-matched healthy controls were automatically transcribed by a speech recognition toolkit. After which, Natural Language Processing techniques were applied to automatically extract the lexical features and document vectors of transcriptions. Using these features, we applied ensemble machine learning algorithm (by leave-one-out cross-validation) to predict the Negative Symptom Assessment subject ratings of schizophrenic patients, and to classify patients from controls, achieving a maximum accuracy of 78.7%. These results indicate that schizophrenic patients exhibit significant differences in lexical usage compared with healthy controls, and the possibility of using these lexical features in the understanding and diagnosis of schizophrenia.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.description.sponsorshipNMRC (Natl Medical Research Council, S’pore)en_US
dc.language.isoenen_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICDSP.2018.8631830en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleAutomatic verbal analysis of interviews with schizophrenic patientsen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolLee Kong Chian School of Medicine (LKCMedicine)en_US
dc.contributor.conference2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)en_US
dc.contributor.researchInstitute for Media Innovation (IMI)en_US
dc.identifier.doi10.1109/ICDSP.2018.8631830-
dc.description.versionAccepted versionen_US
dc.subject.keywordsNLPen_US
dc.subject.keywordsSchizophreniaen_US
dc.citation.conferencelocationShanghai, Chinaen_US
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