MIT researchers dive into the intersections between health and machine learning. Artificial Intelligence uses machine learning to help identify patterns humans may have difficulty identifying. What if we could identify patterns of behavior in those with mental and emotional health difficulties? IoT and AI technologies can give us new ways to collect data and monitor such conditions.
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Deploying Machine Learning to Improve Mental Health
A machine-learning expert and a psychology researcher/clinician may seem an unlikely duo. But MIT’s Rosalind Picard and Massachusetts General Hospital’s Paola Pedrelli are united by the belief that artificial intelligence may be able to help make mental health care more accessible to patients.
In her 15 years as a clinician and researcher in psychology, Pedrelli says “it's been very, very clear that there are a number of barriers for patients with mental health disorders to accessing and receiving adequate care.” Those barriers may include figuring out when and where to seek help, finding a nearby provider who is taking patients, and obtaining financial resources and transportation to attend appointments.
Pedrelli is an assistant professor in psychology at the Harvard Medical School and the associate director of the Depression Clinical and Research Program at Massachusetts General Hospital (MGH). For more than five years, she has been collaborating with Picard, an MIT professor of media arts and sciences and a principal investigator at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) on a project to develop machine-learning algorithms to help diagnose and monitor symptom changes among patients with major depressive disorder.
Machine learning is a type of AI technology where, when the machine is given lots of data and examples of good behavior (i.e., what output to produce when it sees a particular input), it can get quite good at autonomously performing a task. It can also help identify patterns that are meaningful, which humans may not have been able to find as quickly without the machine's help. Using wearable devices and smartphones of study participants, Picard and Pedrelli can gather detailed data on participants’ skin conductance and temperature, heart rate, activity levels, socialization, personal assessment of depression, sleep patterns, and more. Their goal is to develop machine learning algorithms that can intake this tremendous amount of data, and make it meaningful — identifying when an individual may be struggling and what might be helpful to them. They hope that their algorithms will eventually equip physicians and patients with useful information about individual disease trajectory and effective treatment.
“We're trying to build sophisticated models that have the ability to not only learn what's common across people, but to learn categories of what's changing in an individual’s life,” Picard says. “We want to provide those individuals who want it with the opportunity to have access to information that is evidence-based and personalized, and makes a difference for their health.”
