A multimodal approach for predicting changes in PTSD symptom severity

Abstract

The rising prevalence of mental illnesses is increasing the demand for new digital tools to support mental wellbeing. Numerous collaborations spanning the fields of psychology, machine learning and health are building such tools. Machine-learning models that estimate effects of mental health interventions currently rely on either user self-reports or measurements of user physiology. In this paper, we present a multimodal approach that combines self-reports from questionnaires and skin conductance physiology in a web-based trauma-recovery regime. We evaluate our models on the EASE multimodal dataset and create PTSD symptom severity change estimators at both total and cluster-level. We demonstrate that modeling the PTSD symptom severity change at the total-level with self-reports can be statistically significantly improved by the combination of physiology and self-reports or just skin conductance measurements. Our experiments show that PTSD symptom cluster severity changes using our novel multimodal approach are significantly better modeled than using self-reports and skin conductance alone when extracting skin conductance features from triggers modules for avoidance, negative alterations in cognition & mood and alterations in arousal & reactivity symptoms, while it performs statistically similar for intrusion symptom.