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A study participant is interviewed by Ellie, an artificial character, to gather text data. Credit: Jonathan Gratch, USC Institute for Creative Technologies |
University of Alberta researchers have trained a machine learning model to identify people with post-traumatic stress disorder with 80 per cent accuracy by analyzing text data. The model could one day serve as an accessible and inexpensive screening tool to support health professionals in detecting and diagnosing PTSD or other mental health disorders through telehealth platforms.
Psychiatry PhD candidate Jeff Sawalha, who led the project, performed a sentiment analysis of text from a dataset created by Jonathan Gratch at USC’s Institute for Creative Technologies. Sentiment analysis involves taking a large body of data, such as the contents of a series of tweets, and categorizing them — for example, seeing how many are expressing positive thoughts and how many are expressing negative thoughts.
“We wanted to strictly look at the sentiment analysis from this dataset to see if we could properly identify or distinguish individuals with PTSD just using the emotional content of these interviews,” said Sawalha.
The text in the USC dataset was gathered through 250 semi-structured interviews conducted by an artificial character, Ellie, over video conferencing calls with 188 people without PTSD and 87 with PTSD.