Machine learning model predicts health conditions of people with MS during stay-at-home periods

at the end of the semester using smartphone and fitness tracker data. Participants in the earlier study, specifically 138 first-year CMU students, were relatively similar to each other when compared to the larger population beyond the university.

People with MS can experience several chronic comorbidities, which gave the team a chance to test if their model could predict adverse health outcomes such as severe fatigue, poor sleep quality and worsening of MS symptoms in addition to depression. Building on this study, the team hopes to advance precision medicine for people with MS by improving early detection of disease progression and implementing targeted interventions based on digital phenotyping.

The work could also help inform policymakers tasked with issuing future stay-at-home orders or other similar responses during pandemics or natural disasters. When the original COVID-19 stay-at-home orders were issued, there were early concerns about itsbut only a belated appreciation for the toll on peoples' mental and physical health—particularly among vulnerable populations such as those with chronic neurological conditions.

"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," Goel said.

Source: Education Headlines (educationheadlines.net)

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