When Brent Davis came to Western, he was intent on becoming a psychiatrist. However, a knack for bioinformatics put him on a path to impact the wellbeing of others in a different way.
Now a PhD candidate in Western’s Predictive Health Informatics Lab, Davis BMSc’14, and professor Dan Lizotte are using machine learning methods to develop a social media search tool that public health units can use to address mental health and addiction problems in their districts.
While their methods involve artificial intelligence, their motivation comes from a natural desire to help others: both Davis and Lizotte said their health-care pursuits were influenced by their mothers, who were nurses.
“My mother was a mental health nurse for 30 years,” Davis said. “Watching her experience, I learned that mental health and substance abuse can be linked. I grew up around stories of people falling through the cracks and seeing her go above and beyond to go help those populations.”
Knowing how the demand for mental health services can be underestimated, they took the approach of “can we make the case that there’s this many people out there suffering, and show evidence for it?” Davis said.
Lizotte, who is jointly appointed to the department of computer science in the Faculty of Science and the department of epidemiology and biostatistics in the Schulich School of Medicine & Dentistry, gains insight into the challenges faced by public health practitioners through his connections across the province.
“Public health practitioners have some key questions around the population they serve,” he said. “They want to know who is vulnerable to certain kinds of issues and what those issues are.”
In collaboration with Cameron McDermaid, an epidemiologist with Ottawa Public Health, Lizotte is hoping to use AI techniques to help those with living experience of opioid dependence, a group that is difficult to identify and offer services. Health units’ traditional information-gathering techniques, such as face-to-face conversations and surveys, may not be the best means to reach that group, but social media could.
However, public health practitioners cannot achieve that simply by searching social media platforms — not only do they not have time, but there are other complex challenges including the sheer volume of posts.
“We’re looking at developing data analysis methods that both accommodate the fact that it’s a lot of data that’s not feasible to go through by hand, and that it doesn’t exclusively tell you if a person is suffering from addiction or at risk,” said Lizotte. “But maybe, at the population level, we can let the health-care practitioners know there is some prevalence, some proportion of their population that’s at risk for this outcome or is facing this challenge.”
Another major challenge, and the subject of Davis’s dissertation, is the ever-evolving language of people who use drugs.
“There’s a very old problem called the ‘unknown,’” Davis said. “It is a vocabulary problem in that an expert in the field can’t know all the slang, jargon and obscure words for common drug terms and use existing keyboard-based search techniques … You can guess the slang term for fentanyl might be ‘fent,’ but that’s a guess.”
Davis and Lizotte developed a new language-processing technique called Archetype-Based Modeling and Search of Social Media to identify and pull key words and phrases from the Reddit forum r/opiates, which provides a fairly accurate indication of terms used by people most likely to face addiction issues. They then search other platforms for similar words based on a specified set of archetypes from which both vocabulary and relevance information are extracted.
In addition to “fent,” the team discovered other jargon included xan, carfent, bupe, smack, trams, rerock, xfer, noids and rock.
They also found additional and unexpected information.
“One of the emerging themes we found when looking for words around opioid use is that the word cocaine is in there, meth is in there, amphetamines are in there, along with other prescription drugs that aren’t under the opioid class,” Davis said. “It paints the picture that the lived experience of these people is not necessarily a one-substance issue.”
Such revelations could, with further validation, affect treatment approaches, but that’s not the focus for David and Lizotte.
“This is complementary information or data,” Lizotte said. “Our goal is to provide public-health practitioners with better situational awareness around the populations they serve, and the best evidence for decision support.”
The team believes there will be opportunities to research social co-occurrences, investigate comorbidities within populations, and understand social challenges and needs beyond opioid use.
And they have already used the language-processing technique to search Twitter for clues on the state of people’s mental health during COVID-19. They’ve noticed that while most conversations express frustrations and doubt, there are also positive feelings being shared, including increased family-time activities.
“Part of the journey is seeing that it works and then asking, is it helpful, responsible, and what are we going to do with it?” Davis said.