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Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk
Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411603/ https://www.ncbi.nlm.nih.gov/pubmed/37564247 http://dx.doi.org/10.3389/fpsyt.2023.1186569 |
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author | Parsapoor (Mah Parsa), Mahboobeh Koudys, Jacob W. Ruocco, Anthony C. |
author_facet | Parsapoor (Mah Parsa), Mahboobeh Koudys, Jacob W. Ruocco, Anthony C. |
author_sort | Parsapoor (Mah Parsa), Mahboobeh |
collection | PubMed |
description | Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transition from suicidal thoughts to suicide attempts. Advances in artificial intelligence have revolutionized the development of suicide screening tools and suicide risk detection systems. Thus, various types of AI systems, including text-based systems, have been proposed to identify individuals at risk of suicide. Although these systems have shown acceptable performance, most of them have not incorporated suicide theories in their design. Furthermore, directly applying suicide theories may be difficult because of the diversity and complexity of these theories. To address these challenges, we propose an approach to develop speech- and language-based suicide risk detection systems. We highlight the promise of establishing a benchmark textual and vocal dataset using a standardized speech and language assessment procedure, and research designs that distinguish between the risk factors for suicide attempt above and beyond those for suicidal ideation alone. The benchmark dataset could be used to develop trustworthy machine learning or deep learning-based suicide risk detection systems, ultimately constructing a foundation for vocal and textual-based suicide risk detection systems. |
format | Online Article Text |
id | pubmed-10411603 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104116032023-08-10 Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk Parsapoor (Mah Parsa), Mahboobeh Koudys, Jacob W. Ruocco, Anthony C. Front Psychiatry Psychiatry Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transition from suicidal thoughts to suicide attempts. Advances in artificial intelligence have revolutionized the development of suicide screening tools and suicide risk detection systems. Thus, various types of AI systems, including text-based systems, have been proposed to identify individuals at risk of suicide. Although these systems have shown acceptable performance, most of them have not incorporated suicide theories in their design. Furthermore, directly applying suicide theories may be difficult because of the diversity and complexity of these theories. To address these challenges, we propose an approach to develop speech- and language-based suicide risk detection systems. We highlight the promise of establishing a benchmark textual and vocal dataset using a standardized speech and language assessment procedure, and research designs that distinguish between the risk factors for suicide attempt above and beyond those for suicidal ideation alone. The benchmark dataset could be used to develop trustworthy machine learning or deep learning-based suicide risk detection systems, ultimately constructing a foundation for vocal and textual-based suicide risk detection systems. Frontiers Media S.A. 2023-07-24 /pmc/articles/PMC10411603/ /pubmed/37564247 http://dx.doi.org/10.3389/fpsyt.2023.1186569 Text en Copyright © 2023 Parsapoor (Mah Parsa), Koudys and Ruocco. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Psychiatry Parsapoor (Mah Parsa), Mahboobeh Koudys, Jacob W. Ruocco, Anthony C. Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk |
title | Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk |
title_full | Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk |
title_fullStr | Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk |
title_full_unstemmed | Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk |
title_short | Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk |
title_sort | suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411603/ https://www.ncbi.nlm.nih.gov/pubmed/37564247 http://dx.doi.org/10.3389/fpsyt.2023.1186569 |
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