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Timely prediction of clinically critical events in Intensive Care Unit (ICU)is important for improving care and survival rate. Most of the existingapproaches are based on the application of various classification methods onexplicitly extracted statistical features from vital signals. In this work, wepropose to eliminate the high cost of engineering hand-crafted features frommultivariate time-series of physiologic signals by learning theirrepresentation with a sequence-to-sequence auto-encoder. We then propose tohash the learned representations to enable signal similarity assessment for theprediction of critical events. We apply this methodological framework topredict Acute Hypotensive Episodes (AHE) on a large and diverse dataset ofvital signal recordings. Experiments demonstrate the ability of the presentedframework in accurately predicting an upcoming AHE.

Multivariate Time-series Similarity Assessment via Unsupervised  Representation Learning and Stratified Locality Sensitive Hashing:  Application to Early Acute Hypotensive Episode Detection