A comprehensive review of computational methods for automatic prediction of schizophrenia with insight into indigenous populations

Loading...
Thumbnail Image
Other Title
Authors
Randall, Ratana
Sharifzadeh, Hamid
Krishnan, J.
Pang, Shaoning
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2019-09-12
Supervisors
Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
automated speech analysis
schizophrenia
psychosis
diagnosis
computational methods
language dysfunction
language relativity
cross-cultural studies
Kincaid, D. Lawrence, (1945-)
convergence model of communication
Māori
mental health
speech analysis
Citation
Ratana, 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.2019.00659
Abstract
Psychiatrists 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.
Publisher
Frontiers Media (parent company: Holtzbrinck Publishing Group)
Link to ePress publication
DOI
doi:10.3389/fpsyt.2019.00659
Copyright holder
© 2019 Ratana, Sharifzadeh, Krishnan and Pang.
Copyright notice
This 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.
Copyright license
Available online at
This item appears in: