Understanding Language Abnormalities and Associated Clinical Markers in Psychosis: The Promise of Computational Methods
Abstract The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early in...
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| Vydáno v: | Schizophrenia bulletin Ročník 47; číslo 2; s. 344 - 362 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Oxford University Press
16.03.2021
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| ISSN: | 0586-7614, 1745-1701, 1745-1701 |
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| Abstract | Abstract
The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment—manual annotations and/or clinical rating scales—are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis. |
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| AbstractList | The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment-manual annotations and/or clinical rating scales-are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis.The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment-manual annotations and/or clinical rating scales-are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis. The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment—manual annotations and/or clinical rating scales—are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis. Abstract The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment—manual annotations and/or clinical rating scales—are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis. The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment—manual annotations and/or clinical rating scales—are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis. |
| Author | Hitczenko, Kasia Mittal, Vijay A Goldrick, Matthew |
| AuthorAffiliation | 1 Department of Linguistics, Northwestern University , Evanston, IL 4 Institute for Policy Research, Northwestern University , Evanston, IL 6 Institute for Innovations in Developmental Sciences, Northwestern University , Evanston and Chicago, IL 2 Department of Psychology, Northwestern University , Evanston, IL 5 Medical Social Sciences, Northwestern University , Chicago, IL 3 Department of Psychiatry, Northwestern University , Chicago, IL |
| AuthorAffiliation_xml | – name: 4 Institute for Policy Research, Northwestern University , Evanston, IL – name: 3 Department of Psychiatry, Northwestern University , Chicago, IL – name: 1 Department of Linguistics, Northwestern University , Evanston, IL – name: 6 Institute for Innovations in Developmental Sciences, Northwestern University , Evanston and Chicago, IL – name: 2 Department of Psychology, Northwestern University , Evanston, IL – name: 5 Medical Social Sciences, Northwestern University , Chicago, IL |
| Author_xml | – sequence: 1 givenname: Kasia surname: Hitczenko fullname: Hitczenko, Kasia email: kasia.hitczenko@northwestern.edu organization: Department of Linguistics, Northwestern University, Evanston, IL – sequence: 2 givenname: Vijay A surname: Mittal fullname: Mittal, Vijay A organization: Department of Psychology, Northwestern University, Evanston, IL – sequence: 3 givenname: Matthew surname: Goldrick fullname: Goldrick, Matthew organization: Department of Linguistics, Northwestern University, Evanston, IL |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33205155$$D View this record in MEDLINE/PubMed |
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| Copyright | The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com. The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com 2020 |
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| Keywords | automated linguistic analysis computational linguistics thought disorder speech language production |
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The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be... The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to... The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to... |
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| Title | Understanding Language Abnormalities and Associated Clinical Markers in Psychosis: The Promise of Computational Methods |
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