Improving Clinical Predictions through Unsupervised Time Series Representation Learning

2 Dec 2018Xinrui LyuMatthias HueserStephanie L. HylandGeorge ZerveasGunnar Raetsch

In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making. By evaluating on the prediction of clinically relevant outcomes, we show that in a practical setting, unsupervised representation learning can offer clear performance benefits over end-to-end supervised architectures... (read more)

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