Distributed Stratified Locality Sensitive Hashing For Critical Event Prediction In The Cloud
Alessandro de Palma, Erik Hemberg, Una-May O'Reilly . Arxiv 2017 – 1 citation
[Paper]
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.