Researchers at Carnegie Mellon University, the University of Pittsburgh, and the University of Washington developed a machine learning model that can predict the health of chronic neurological disorder patients during stay-at-home periods.
The researchers amassed sensor data from smartphones and fitness trackers of multiple sclerosis (MS) patients before and during the early COVID-19 surge. They fed the data into the model to anticipate depression, fatigue, poor sleep quality, and exacerbated MS symptoms. Their work is published in the Journal of Medical Internet Research Mental Health.
"We were able to capture the change in people's behaviors and accurately predict clinical outcomes when they are forced to stay at home for prolonged periods," says Mayank Goel, head of the Smart Sensing for Humans Lab at CMU. "Now that we have a working model, we could evaluate who is at risk for worsening mental health or physical health, inform clinical triage decisions, or shape future public health policies."
From Carnegie Mellon University
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Abstracts Copyright © 2022 SmithBucklin, Washington, DC, USA
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