  We train and validate a semi-supervised, multi-task LSTM on 57,675
person-weeks of data from off-the-shelf wearable heart rate sensors, showing
high accuracy at detecting multiple medical conditions, including diabetes
(0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep
apnea (0.8298). We compare two semi-supervised train- ing methods,
semi-supervised sequence learning and heuristic pretraining, and show they
outperform hand-engineered biomarkers from the medical literature. We believe
our work suggests a new approach to patient risk stratification based on
cardiovascular risk scores derived from popular wearables such as Fitbit, Apple
Watch, or Android Wear.

Machine Learning;Artificial Intelligence;Machine Learning