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 training
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.