arXiv:1802.02511cs.LG
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
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.
Here's a more in depth argument of why maybe you shouldn't be: https://www.reddit.com/r/apple/comments/9fkb3t/im_an_emergen... "Medical grade" is hard to define, but you might actually be very surprised by how accurate this simple sensor could be in an era of deep learning AI.…
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