Machine Learning and Mental Health

Picard joined the MIT Media Lab in 1991. Three years later, he published a book, "Affective Computing", which inspired the development of the field of that name. Affect computing is now a strong area of research concerned with developing technologies that can measure, understand and model data related to people's emotions.

While early research focused on determining whether machine learning could use the data to identify a participant's current emotions, Picard and Pedrelli's current work at MIT's Jamil Clinic goes several steps further. They want to know if machine learning can predict disorder trajectories, identify changes in a person's behavior, and provide data that informs personalized medical care.

Picard and Szymon Fedor, a research scientist in Picard's affective computing lab, began collaborating with Pedrelli in 2016. After running a small pilot study, they are now in the fourth year of their National Institutes of Health, five-year study.

To conduct the study, researchers recruited MGH participants with major depressive disorder who had recently changed their treatment. So far, 48 participants have enrolled in the study. For 22 hours per day, every day for 12 weeks, participants wore the Empatica E4 wristband. These wearable wristbands, designed by one of the companies Picard founded, can capture biometric data such as electrodermal (skin) activity. Participants also download apps to their phones that collect data on text and phone calls, location and app use, and also prompt them to complete biweekly depression surveys.

Each week, patients check in with a therapist who assesses their depressive symptoms.

Picard says, “We put all the data we collect from wearables and smartphones into our machine-learning algorithms, and we try to see how well the machine learning predicts the labels doctors give us. " "At the moment, we're pretty good at predicting those labels."

For more info : https://nearlearn.com/machine-learning-classroom-training-in-bangalore-india