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dc.contributor.authorRandall, Ratana
dc.contributor.authorSharifzadeh, Hamid
dc.contributor.authorKrishnan, J.
dc.contributor.authorPang, Shaoning
dc.date.accessioned2019-10-28T19:36:41Z
dc.date.available2019-10-28T19:36:41Z
dc.date.issued2019-09-12
dc.identifier.issn1664-0640
dc.identifier.urihttps://hdl.handle.net/10652/4763
dc.description.abstractPsychiatrists rely on language and speech behavior as one of the main clues in psychiatric diagnosis. Descriptive psychopathology and phenomenology form the basis of a common language used by psychiatrists to describe abnormal mental states. This conventional technique of clinical observation informed early studies on disturbances of thought form, speech, and language observed in psychosis and schizophrenia. These findings resulted in language models that were used as tools in psychosis research that concerned itself with the links between formal thought disorder and language disturbances observed in schizophrenia. The end result was the development of clinical rating scales measuring severity of disturbances in speech, language, and thought form. However, these linguistic measures do not fully capture the richness of human discourse and are time-consuming and subjective when measured against psychometric rating scales. These linguistic measures have not considered the influence of culture on psychopathology. With recent advances in computational sciences, we have seen a re-emergence of novel research using computing methods to analyze free speech for improving prediction and diagnosis of psychosis. Current studies on automated speech analysis examining for semantic incoherence are carried out based on natural language processing and acoustic analysis, which, in some studies, have been combined with machine learning approaches for classification and prediction purposes.en_NZ
dc.language.isoenen_NZ
dc.publisherFrontiers Media (parent company: Holtzbrinck Publishing Group)en_NZ
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_NZ
dc.subjectautomated speech analysisen_NZ
dc.subjectschizophreniaen_NZ
dc.subjectpsychosisen_NZ
dc.subjectdiagnosisen_NZ
dc.subjectcomputational methodsen_NZ
dc.subjectlanguage dysfunctionen_NZ
dc.subjectlanguage relativityen_NZ
dc.subjectcross-cultural studiesen_NZ
dc.subjectKincaid, D. Lawrence, (1945-)en_NZ
dc.subjectconvergence model of communicationen_NZ
dc.subjectMāorien_NZ
dc.subjectmental healthen_NZ
dc.subjectspeech analysisen_NZ
dc.titleA comprehensive review of computational methods for automatic prediction of schizophrenia with insight into indigenous populationsen_NZ
dc.typeJournal Articleen_NZ
dc.date.updated2019-10-28T13:30:11Z
dc.rights.holder© 2019 Ratana, Sharifzadeh, Krishnan and Pang.en_NZ
dc.identifier.doidoi:10.3389/fpsyt.2019.00659en_NZ
dc.subject.marsden200402 Computational Linguisticsen_NZ
dc.subject.marsden111714 Mental Healthen_NZ
dc.subject.marsden200209 Multicultural, Intercultural and Cross-cultural Studiesen_NZ
dc.identifier.bibliographicCitationRatana, R., Sharifzadeh, H., Krishnan, J., & Pang, S. H. (2019). A Comprehensive Review of Computational Methods for Automatic Prediction of Schizophrenia With Insight Into Indigenous Populations. Frontiers in Psychiatry, 10, 659. doi:10.3389/fpsyt.2019.00659en_NZ
unitec.publication.spage659en_NZ
unitec.publication.volume10en_NZ
unitec.publication.titleFrontiers in Psychiatryen_NZ
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
dc.contributor.affiliationBay of Plenty District Health Board (N.Z.)en_NZ
unitec.identifier.roms64580en_NZ
dc.subject.tukutukuHauora hinengaroen_NZ
unitec.publication.placeLausanne, Switzerlanden_NZ
unitec.institution.studyareaComputing


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