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A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations
BACKGROUND: The rise of depression, anxiety, and suicide rates has led to increased demand for telemedicine-based mental health screening and remote patient monitoring (RPM) solutions to alleviate the burden on, and enhance the efficiency of, mental health practitioners. Multimodal dialog systems (M...
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/PMC10520716/ https://www.ncbi.nlm.nih.gov/pubmed/37767217 http://dx.doi.org/10.3389/fpsyg.2023.1135469 |
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author | Cohen, Joshua Richter, Vanessa Neumann, Michael Black, David Haq, Allie Wright-Berryman, Jennifer Ramanarayanan, Vikram |
author_facet | Cohen, Joshua Richter, Vanessa Neumann, Michael Black, David Haq, Allie Wright-Berryman, Jennifer Ramanarayanan, Vikram |
author_sort | Cohen, Joshua |
collection | PubMed |
description | BACKGROUND: The rise of depression, anxiety, and suicide rates has led to increased demand for telemedicine-based mental health screening and remote patient monitoring (RPM) solutions to alleviate the burden on, and enhance the efficiency of, mental health practitioners. Multimodal dialog systems (MDS) that conduct on-demand, structured interviews offer a scalable and cost-effective solution to address this need. OBJECTIVE: This study evaluates the feasibility of a cloud based MDS agent, Tina, for mental state characterization in participants with depression, anxiety, and suicide risk. METHOD: Sixty-eight participants were recruited through an online health registry and completed 73 sessions, with 15 (20.6%), 21 (28.8%), and 26 (35.6%) sessions screening positive for depression, anxiety, and suicide risk, respectively using conventional screening instruments. Participants then interacted with Tina as they completed a structured interview designed to elicit calibrated, open-ended responses regarding the participants' feelings and emotional state. Simultaneously, the platform streamed their speech and video recordings in real-time to a HIPAA-compliant cloud server, to compute speech, language, and facial movement-based biomarkers. After their sessions, participants completed user experience surveys. Machine learning models were developed using extracted features and evaluated with the area under the receiver operating characteristic curve (AUC). RESULTS: For both depression and suicide risk, affected individuals tended to have a higher percent pause time, while those positive for anxiety showed reduced lip movement relative to healthy controls. In terms of single-modality classification models, speech features performed best for depression (AUC = 0.64; 95% CI = 0.51–0.78), facial features for anxiety (AUC = 0.57; 95% CI = 0.43–0.71), and text features for suicide risk (AUC = 0.65; 95% CI = 0.52–0.78). Best overall performance was achieved by decision fusion of all models in identifying suicide risk (AUC = 0.76; 95% CI = 0.65–0.87). Participants reported the experience comfortable and shared their feelings. CONCLUSION: MDS is a feasible, useful, effective, and interpretable solution for RPM in real-world clinical depression, anxiety, and suicidal populations. Facial information is more informative for anxiety classification, while speech and language are more discriminative of depression and suicidality markers. In general, combining speech, language, and facial information improved model performance on all classification tasks. |
format | Online Article Text |
id | pubmed-10520716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105207162023-09-27 A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations Cohen, Joshua Richter, Vanessa Neumann, Michael Black, David Haq, Allie Wright-Berryman, Jennifer Ramanarayanan, Vikram Front Psychol Psychology BACKGROUND: The rise of depression, anxiety, and suicide rates has led to increased demand for telemedicine-based mental health screening and remote patient monitoring (RPM) solutions to alleviate the burden on, and enhance the efficiency of, mental health practitioners. Multimodal dialog systems (MDS) that conduct on-demand, structured interviews offer a scalable and cost-effective solution to address this need. OBJECTIVE: This study evaluates the feasibility of a cloud based MDS agent, Tina, for mental state characterization in participants with depression, anxiety, and suicide risk. METHOD: Sixty-eight participants were recruited through an online health registry and completed 73 sessions, with 15 (20.6%), 21 (28.8%), and 26 (35.6%) sessions screening positive for depression, anxiety, and suicide risk, respectively using conventional screening instruments. Participants then interacted with Tina as they completed a structured interview designed to elicit calibrated, open-ended responses regarding the participants' feelings and emotional state. Simultaneously, the platform streamed their speech and video recordings in real-time to a HIPAA-compliant cloud server, to compute speech, language, and facial movement-based biomarkers. After their sessions, participants completed user experience surveys. Machine learning models were developed using extracted features and evaluated with the area under the receiver operating characteristic curve (AUC). RESULTS: For both depression and suicide risk, affected individuals tended to have a higher percent pause time, while those positive for anxiety showed reduced lip movement relative to healthy controls. In terms of single-modality classification models, speech features performed best for depression (AUC = 0.64; 95% CI = 0.51–0.78), facial features for anxiety (AUC = 0.57; 95% CI = 0.43–0.71), and text features for suicide risk (AUC = 0.65; 95% CI = 0.52–0.78). Best overall performance was achieved by decision fusion of all models in identifying suicide risk (AUC = 0.76; 95% CI = 0.65–0.87). Participants reported the experience comfortable and shared their feelings. CONCLUSION: MDS is a feasible, useful, effective, and interpretable solution for RPM in real-world clinical depression, anxiety, and suicidal populations. Facial information is more informative for anxiety classification, while speech and language are more discriminative of depression and suicidality markers. In general, combining speech, language, and facial information improved model performance on all classification tasks. Frontiers Media S.A. 2023-09-11 /pmc/articles/PMC10520716/ /pubmed/37767217 http://dx.doi.org/10.3389/fpsyg.2023.1135469 Text en Copyright © 2023 Cohen, Richter, Neumann, Black, Haq, Wright-Berryman and Ramanarayanan. 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 | Psychology Cohen, Joshua Richter, Vanessa Neumann, Michael Black, David Haq, Allie Wright-Berryman, Jennifer Ramanarayanan, Vikram A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations |
title | A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations |
title_full | A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations |
title_fullStr | A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations |
title_full_unstemmed | A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations |
title_short | A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations |
title_sort | multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520716/ https://www.ncbi.nlm.nih.gov/pubmed/37767217 http://dx.doi.org/10.3389/fpsyg.2023.1135469 |
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