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Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood

Published online by Cambridge University Press:  03 August 2020

Katharina Schultebraucks Open the ORCID record for Katharina Schultebraucks [Opens in a new window] ,
Vijay Yadav ,
Arieh Y. Shalev ,
George A. Bonanno  and
Isaac R. Galatzer-Levy
Katharina Schultebraucks*
Affiliation:
Department of Emergency Medicine, Vagelos School of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, USA Data Science Institute, Columbia University, New York, New York, USA
Vijay Yadav
Affiliation:
AiCure, New York, New York, USA
Arieh Y. Shalev
Affiliation:
Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, USA
George A. Bonanno
Affiliation:
Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, New York, USA
Isaac R. Galatzer-Levy
Affiliation:
Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, USA AiCure, New York, New York, USA
*
Author for correspondence: Katharina Schultebraucks, E-mail: [email protected]
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Abstract

Background

Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD).

Methods

N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally.

Results

Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82).

Conclusions

Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.

Keywords

Computer visiondeep learningdepressiondigital biomarkeremergency departmentlandmark featureposttraumatic stressresiliencevoice analysis
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 5 , April 2022 , pp. 957 - 967
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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