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Scientists ­se AI to Predict Biological Age Based on Smartphone, Wearables Data


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Smartphone handsets display health and lifespan data.

Researchers from the Moscow Institute of Physics and Technology have shown that physical activity data acquired from wearable computing devices can be used to produce digital biomarkers of aging and frailty.

Credit: MIPT News

Moscow Institute of Physics and Technology (MIPT) researchers in Russia have demonstrated that physical activity data obtained from wearable computing devices can be used to generate digital biomarkers of aging and frailty.

"Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of the arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records," notes MIPT's Peter Fedichev. "Inspired by these examples, we explored AI potential for Health Risks Assessment based on human physical activity."

The scientists analyzed physical activity records and clinical data from the U.S. National Health and Nutrition Examination Survey, and trained a neural network to predict biological age and mortality risk of participants in a week-long stream of activity measurements.

A convolutional neural network was used to deconstruct the most biologically relevant motion patterns and establish their relation to general health and recorded lifespan.

From MIPT News (Russia)
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Abstracts Copyright © 2018 Information Inc., Bethesda, Maryland, USA


 

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