1 research outputs found
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals
Hundreds of millions of surgical procedures take place annually across the
world, which generate a prevalent type of electronic health record (EHR) data
comprising time series physiological signals. 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. We evaluate PHASE on minute-by-minute
EHR data of more than 50,000 surgeries from two operating room (OR) datasets
and patient stays in an intensive care unit (ICU) dataset. PHASE outperforms
other state-of-the-art approaches, such as long-short term memory networks
trained on raw data and gradient boosted trees trained on handcrafted features,
in predicting five distinct outcomes: hypoxemia, hypocapnia, hypotension,
hypertension, and phenylephrine administration. In a transfer learning setting
where we train embedding models in one dataset then embed signals and predict
adverse events in unseen data, PHASE achieves significantly higher prediction
accuracy at lower computational cost compared to conventional approaches.
Finally, given the importance of understanding models in clinical applications
we demonstrate that PHASE is explainable and validate our predictive models
using local feature attribution methods