Machine learning could help predict risk of preterm birth using wearables data
Researchers applied machine learning methods to streams of data about sleep patterns and activity levels from devices worn by pregnant women to predict risk of premature birth.
For all their potential to drive changes in health, wearables have struggled to gain a foothold in medicine. The dramatic changes during pregnancy are a fertile ground to test their potential, though — and new research shows how applying machine learning methods to streams of data from wearable devices could be used to understand the mystery of premature birth.
Machine learning researchers at Stanford University used a deep learning model to analyze wearable activity and sleep data from pregnant participants. No surprise: Their sleep typically got worse and their activity slowed down over the course of pregnancy. But some participants had data profiles that didn’t match their pregnancy stage — and it was those pregnancies, the researchers found, that were more likely to result in a preterm birth.
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