Deep Transfer Learning for Physiological Signals

Hugh Chen, Scott Lundberg, Gabe Erion, Jerry H. Kim, Su-In Lee

Abstract: Deep learning is increasingly common in healthcare, yet transfer learning for physiological signals (e.g., temperature, heart rate, etc.) is under-explored. Here, we present a straightforward, yet performant framework for transferring knowledge about physiological signals. Our framework is called PHASE (\underline{PH}ysiologic\underline{A}l \underline{S}ignal \underline{E}mbeddings). It i) learns deep embeddings of physiological signals and ii) predicts adverse outcomes based on the embeddings. PHASE is the first instance of deep transfer learning in a cross-hospital, cross-department setting for physiological signals. We show that PHASE's per-signal (one for each signal) LSTM embedding functions confer a number of benefits including improved performance, successful transference between hospitals, and lower computational cost.