What really matters - An information gain analysis of questions and reactions in automated PTSD screenings

Post-traumatic stress disorder (PTSD) is a mental health condition which severely affects society on many levels. However, PTSD remains often undiagnosed and untreated. To improve screening access and acceptance, a fully automated virtual human was designed to conduct many PTSD screening interviews...

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Bibliographic Details
Published inInternational Conference on Affective Computing and Intelligent Interaction and workshops pp. 15 - 20
Main Authors Wortwein, Torsten, Scherer, Stefan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Acoustics
Feature extraction
Interviews
Medical services
Principal component analysis
Support vector machines
Training
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Summary:Post-traumatic stress disorder (PTSD) is a mental health condition which severely affects society on many levels. However, PTSD remains often undiagnosed and untreated. To improve screening access and acceptance, a fully automated virtual human was designed to conduct many PTSD screening interviews (N=198). Here, we investigate which questions elicit the most indicative multimodal behaviors for PTSD. To this effect, we employ an information gain driven ranking procedure to identify the most informative questions. Further, we look into question dependent behaviors. To evaluate the question ranking, we investigate the discriminative faculty of the top questions using support vector machines. Our results reveal that only a subset of posed questions are required to robustly detect symptoms of PTSD in subject-independent machine learning experiments. We observe strong performance and can confirm that many of the identified behaviors correspond to commonly found behavioral indicators related to PTSD and depression.
ISSN:2156-8111
DOI:10.1109/ACII.2017.8273573