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arXiv · 1612.00962

Positive blood culture detection in time series data using a BiLSTM network

Leen De Baets·Joeri Ruyssinck·Thomas Peiffer·Johan Decruyenaere·Filip De Turck·Femke Ongenae·Tom Dhaene
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Abstract

The presence of bacteria or fungi in the bloodstream of patients is abnormal and can lead to life-threatening conditions. A computational model based on a bidirectional long short-term memory artificial neural network, is explored to assist doctors in the intensive care unit to predict whether examination of blood cultures of patients will return positive. As input it uses nine monitored clinical parameters, presented as time series data, collected from 2177 ICU admissions at the Ghent University Hospital. Our main goal is to determine if general machine learning methods and more specific, temporal models, can be used to create an early detection system. This preliminary research obtains an area of 71.95% under the precision recall curve, proving the potential of temporal neural networks in this context.

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Leen De Baets, Joeri Ruyssinck, Thomas Peiffer, Johan Decruyenaere, Filip De Turck, Femke Ongenae, Tom Dhaene. 2016-12-03. Positive blood culture detection in time series data using a BiLSTM network. https://arxiv.org/abs/1612.00962

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