Artificial intelligence can spot the signs of PTSD in your text messages

EDMONTON, Alberta — A text message may be able to reveal if someone is dealing with post-traumatic stress disorder (PTSD), a new study finds. Researchers from the University of Alberta say a machine learning program — a form of artificial intelligence — is capable of “reading between the lines” to find potential warning signs in the way people write.

The team believes this program could become an inexpensive tool that helps mental health professionals detect and diagnose cases of PTSD or other disorders. Psychiatry PhD candidate Jeff Sawalha performed a sentiment analysis of texts using a dataset created by Jonathan Gratch from USC’s Institute for Creative Technologies.

Study authors explain that a sentiment analysis takes a large amount of data and categorizes it. In this case, the model took a massive amount of texts and sorted them according to positive and negative thoughts.

“We wanted to strictly look at the sentiment analysis from this dataset to see if we could properly identify individuals with PTSD just using the emotional content of these interviews,” Sawalha says in a university release.

PTSD texts are neutral or numb

The text sampling came from 250 semi-structured interviews conducted by an artificial interviewer (Ellie) who spoke with real participants using video conferencing calls. Eighty-seven people had PTSD while the other 188 did not.

From their text responses, the team was able to identify people with PTSD through their scores reflecting how often their words displayed neutral or negative thoughts.

“This is in line with a lot of the literature around emotion and PTSD. Some people tend to be neutral, numbing their emotions and maybe not saying too much. And then there are others who express their negative emotions,” Sawalha says.

Study authors note that this process isn’t black and white. For example, a phrase like “I didn’t hate that” could be confusing for the algorithm. Despite that, the machine learning system was able to detect PTSD patients with 80 percent accuracy.

“Text data is so ubiquitous, it’s so available, you have so much of it,” Sawalha continues. “From a machine learning perspective, with this much data, it may be better able to learn some of the intricate patterns that help differentiate people who have a particular mental illness.”

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Finding different and cheaper ways of detecting mental disorders

The team is planning to integrate other types of data, including speech patterns and human motions, which they say may help the system spot mental health disorders better. Moreover, signs of neurological conditions like Alzheimer’s disease are detectable through a person’s ability to speak.

“Unlike an MRI that takes an experienced person to look at it, this is something people can do themselves. I think that’s the direction medicine is probably going, toward more screening tools,” says Russ Greiner, a professor in the Department of Computing Science.

“Having tools like this going forward could be beneficial in a post-pandemic world,” Sawalha concludes.

The study is published in the journal Frontiers in Psychiatry.

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About the Author

Chris Melore

Chris Melore has been a writer, researcher, editor, and producer in the New York-area since 2006. He won a local Emmy award for his work in sports television in 2011.

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  1. I read a book about AI, both the good and the bad. I think AI is an amazing tool, but we don’t understand it, especially the average person has no idea what is going on with AI, or what AI even means.

    I’d simplify AI and say it is a new way of generating and analyzing statistics with the help of advanced math and computer science. Remember that old saying you can lie with statistics? Well, in this case you might be lying by using AI and not even know it.

    An example is the military wanted to generate a way to detect tanks laying in wait in a forest, so they took aerial photos of forests with tanks hidden in them, and forests with no tanks in them, and fed them through a neural net to generate an AI method of doing reconnaissance with automation.

    The problem was that what they were actually measuring was sunlight, because all the photos with tanks in them were taken on sunny days, and the photos without the tanks were taken on cloudy days.

    That is a simple example to give an example of the kinds of issues that AI has. Like statistics or any of the modern technological marvels we have today, there are problems that are glossed over, and people who will use the technology against us, or try to trick us with AI.

    When humans are not even intelligent enough to prevent our only planet from overheating and maybe killing all life, we might want to think about all these new technologies that are being foisted on us in different and instrusive ways.

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