Forecasting adverse surgical events using self-supervised transfer learning for physiological signals

no code implementations • 12 Feb 2020 • Hugh Chen, Scott Lundberg, Gabe Erion, Jerry H. Kim, Su-In Lee

Here, we present a transferable embedding method (i. e., a method to transform time series signals into input features for predictive machine learning models) named PHASE (PHysiologicAl Signal Embeddings) that enables us to more accurately forecast adverse surgical outcomes based on physiological signals.

Time Series Time Series Analysis +1

Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models

no code implementations • ICLR 2019 • Hugh Chen, Scott Lundberg, Gabe Erion, Su-In Lee

Here, we present the PHASE (PHysiologicAl Signal Embeddings) framework, which consists of three components: i) learning neural network embeddings of physiological signals, ii) predicting outcomes based on the learned embedding, and iii) interpreting the prediction results by estimating feature attributions in the "stacked" models (i. e., feature embedding model followed by prediction model).

Network Embedding