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Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
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Detecting Presence of PTSD Using Sentiment Analysis From Text Data.

Jeff Sawalha1,2,3, Muhammad Yousefnezhad1,2,3, Zehra Shah2,3

  • 1Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.

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|February 18, 2022
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Summary

Machine learning models can detect Post-traumatic stress disorder (PTSD) using sentiment analysis of speech from virtual interviews. This offers an accessible tool for mental health screening during the COVID-19 pandemic.

Keywords:
emotionlanguagemachine learningnatural language processingpost-traumatic stress disorder (PTSD)sentiment analysis (SA)telepsychiatry

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Area of Science:

  • Psychiatry and Mental Health
  • Computational Linguistics
  • Artificial Intelligence in Healthcare

Background:

  • The COVID-19 pandemic has increased Post-traumatic stress disorder (PTSD) rates, necessitating remote monitoring solutions.
  • Telehealth services have become crucial for accessing mental health support due to pandemic-related isolation and inaccessibility.
  • Existing virtual diagnostic tools may require augmentation for accurate PTSD identification.

Purpose of the Study:

  • To develop and validate a machine learning model for identifying PTSD through sentiment analysis of semi-structured interviews conducted virtually.
  • To assess the efficacy of natural language processing (NLP) in detecting emotional indicators of PTSD in spoken language.
  • To evaluate the generalizability of the developed model for widespread clinical application.

Main Methods:

  • A machine learning model was trained on text data from semi-structured interviews within the AVEC-19 corpus.
  • Sentiment analysis techniques were applied to extract emotional content from interview transcripts.
  • The model was evaluated on a held-out dataset, achieving a balanced accuracy of 80.4%.

Main Results:

  • The machine learning model demonstrated an 80.4% balanced accuracy in identifying individuals with PTSD.
  • Sentiment analysis of speech successfully identified PTSD presence in a virtual interview setting.
  • Model generalizability was confirmed through various partitioning techniques.

Conclusions:

  • Sentiment analysis of speech via NLP is a viable method for detecting PTSD through virtual mediums.
  • This approach offers a potentially accessible, inexpensive tool for early mental health abnormality detection.
  • The findings support the use of AI-driven tools for remote mental health monitoring, particularly during public health crises.