A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations

Joshua Cohen; Vanessa Richter; Michael Neumann; David Black; Allie Haq; Jennifer Wright-Berryman; Vikram Ramanarayanan

doi:10.3389/fpsyg.2023.1135469

A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations

Front Psychol. 2023 Sep 11:14:1135469. doi: 10.3389/fpsyg.2023.1135469. eCollection 2023.

Authors

Joshua Cohen^#¹, Vanessa Richter^#², Michael Neumann², David Black¹, Allie Haq¹, Jennifer Wright-Berryman³, Vikram Ramanarayanan^{2

4}

Affiliations

¹ Clarigent Health, Mason, OH, United States.
² Modality.AI, Inc., San Francisco, CA, United States.
³ Department of Social Work, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH, United States.
⁴ Otolaryngology - Head and Neck Surgery (OHNS), University of California, San Francisco, San Francisco, CA, United States.

^# Contributed equally.

Abstract

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.

Keywords: anxiety; depression; facial features; machine learning; multimodal dialog systems; natural language processing; speech features; suicide.

Grants and funding

This study was funded by Clarigent Health. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.