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The availability of massive healthcare data repositories calls for efficienttools for data-driven medicine. We introduce a distributed system forStratified Locality Sensitive Hashing to perform fast similarity-basedprediction on large medical waveform datasets. Our implementation, for an ICUuse case, prioritizes latency over throughput and is targeted at a cloudenvironment. We demonstrate our system on Acute Hypotensive Episode predictionfrom Arterial Blood Pressure waveforms. On a dataset of $1.37$ million points,we show scaling up to $40$ processors and a $21\times$ speedup in number ofcomparisons to parallel exhaustive search at the price of a $10\%$ Matthewscorrelation coefficient (MCC) loss. Furthermore, if additional MCC loss can betolerated, our system achieves speedups up to two orders of magnitude.

Distributed Stratified Locality Sensitive Hashing for Critical Event Prediction in the Cloud