Distributed Stratified Locality Sensitive Hashing For Critical Event Prediction In The Cloud
De Palma Alessandro, Hemberg Erik, O'reilly Una-may. Arxiv 2017
The availability of massive healthcare data repositories calls for efficient tools for data-driven medicine. We introduce a distributed system for Stratified Locality Sensitive Hashing to perform fast similarity-based prediction on large medical waveform datasets. Our implementation, for an ICU use case, prioritizes latency over throughput and is targeted at a cloud environment. We demonstrate our system on Acute Hypotensive Episode prediction from 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 of comparisons to parallel exhaustive search at the price of a \(10\%\) Matthews correlation coefficient (MCC) loss. Furthermore, if additional MCC loss can be tolerated, our system achieves speedups up to two orders of magnitude.
Similar Work