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. 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. Select one or more references or the entire library and Endnote will scan for full text.
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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). EndNote EndNote Basic is a free light weight online reference manager that can be used on its own, or in conjunction with EndNote Desktop. An Abstract suitable for indexing on PubMed should be included. It is powerful, customizable, and considered the industry standard program of its type. Data deposition: Datasets are available online at /next-generation-shrna-libraries-datasets. University of California, California (UCSF)īiomedical/Bioinformatics, Bio/Medicine, Semisupervised Learning Abstract EndNote is the oldest and best known of the reference managers.
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