Abstract
Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.
Highlights
Post-traumatic stress disorder (PTSD) is a debilitating condition initiated by exposure to traumatic events, whether witnessing the event in-person, indirectly learning that a traumatic event occurred to a loved one, or through repeated exposure to aversive details of said events [1]
For the PTSD Checklist-Civilian version (PCL-C) and PHQ-8 scores, a t-test was used to determined if there was a difference between the non-PTSD and PTSD group means
For the PCL-C scores, a one-way ANOVA was used to determined if there was a difference between the non-PTSD and PTSD group means
Summary
Post-traumatic stress disorder (PTSD) is a debilitating condition initiated by exposure to traumatic events, whether witnessing the event in-person, indirectly learning that a traumatic event occurred to a loved one, or through repeated exposure to aversive details of said events [1]. There is strong evidence that the current severe acute respiratory syndrome coronavirus two (SARS-CoV-2) pandemic can be considered a global traumatic event [2]. Two outcomes have emerged from the SARS-CoV-2 pandemic: [1] There has been a surge in stress-related mental illnesses such as PTSD, in occupational settings [3,4,5,6,7], and [2] many in-person medical appointments have been moved to a digital format [8]. Prior to the global pandemic, a general population survey across 24 countries estimated that 70% of individuals would experience at least one potentially traumatic event (PTE) in their lifetime [1]. Three in every ten survivors of the SARS-CoV-2 virus, two in every ten healthcare
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