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Automated Method for Diagnosing Speech and Language Dysfunction in Schizophrenia

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Book cover Mental Health Informatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 491))

Abstract

Speech and language dysfunction (SLD) is one of the primary symptoms of schizophrenia. However, SLD measures, such as observer-rated scales, are based on clinical experience and are subjective in nature. This study compares two scales—the Thought, Language and Communication Scale (TLC) and the Clinical Language Disorder Rating Scale (CLANG)—with a novel and automated measure called Ex-Ray. The core hypothesis is that Ex-Ray either outperforms the rating scales in terms of accuracy (i.e., differentiating between schizophrenic participants and non-psychotic controls) or performs at the same level. Twenty-minute audio-recorded, unstructured interviews with 54 Singaporean participants (27 schizophrenics and 27 controls) were conducted. The interviews were rated by use of the TLC and CLANG scales. Manually transcribed texts, based on the interviews, were analysed by Ex-Ray. The three methods were then compared. Receiver Operating Characteristic (ROC) curve analysis demonstrated that Ex-Ray differentiated schizophrenic patients from normal subjects with an accuracy rate of 98 %, but did not outperform the scales at a significant level. Even though Ex-Ray is a valid and reliable measure of SLD in schizophrenia, it failed to outperform the rating scales (TLC and CLANG) for two reasons: (1) the unusually high inter-rater reliability; and (2) the uneven ethnic composition of the sample population. In a follow-up study, Ex-Ray performed at a high level even if subject and control groups were comparable in terms of [1] educational background, [2] ethnic composition (including language background) and [3] socio-economic status.

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Acknowledgments

The authors wish to thank all those involved in the data collection: Lynette Tham, Esther Hong, and Sabrina Yeo, and all the mental health professionals at the Singapore Mental Health Association, as well as the participants in this study. Thank you also to Margaret Lech, Adam Vogel, and Susan Wright for providing comments on earlier versions of this document.

Author information

Authors and Affiliations

  1. School of Arts and Social Sciences, James Cook University, Singapore, 574421, Singapore

    Anne Debra Tilaka

  2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, 4072, Australia

    Joachim Diederich

  3. School of Business/IT, James Cook University, Singapore Campus, Singapore, 574421, Singapore

    Insu Song

  4. North Dakota State University, Fargo, ND, 58102, USA

    Ai Ni Teoh

Authors
  1. Anne Debra Tilaka
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  2. Joachim Diederich
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  3. Insu Song
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  4. Ai Ni Teoh
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Correspondence to Anne Debra Tilaka .

Editor information

Editors and Affiliations

  1. Computer Engineering, RMIT University School of Electrical and, Melbourne, Victoria, Australia

    Margaret Lech

  2. Singapore Campus, James Cook University Australia, School of Business and IT, Singapore, Singapore

    Insu Song

  3. Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, California, USA

    Peter Yellowlees

  4. Psychology Network Pty Ltd, Brisbane, Australia

    Joachim Diederich

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Tilaka, A.D., Diederich, J., Song, I., Teoh, A.N. (2014). Automated Method for Diagnosing Speech and Language Dysfunction in Schizophrenia. In: Lech, M., Song, I., Yellowlees, P., Diederich, J. (eds) Mental Health Informatics. Studies in Computational Intelligence, vol 491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38550-6_11

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  • DOI: https://doi.org/10.1007/978-3-642-38550-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38549-0

  • Online ISBN: 978-3-642-38550-6

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