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PhysioNet Challenge 2019 aims to develop novel solutions for early detection the clinical onset of sepsis. We used training date provided and develop machine learning classifiers to predict clinical sepsis 6 - 12 hours ahead of the clinical onset.Neural networks possess abilities to uncover insights from complex datasets. We have trained an ensemble classifier with a convolutional neural network (CNN) and a recurrent neural network (RNN) for early detection of sepsis. The classifiers were implemented in in Python using Keras with Tensorflow as back-end. Both networks were combined using bagging to achieve better performance.The database appeared imbalanced, and the class (positive) with small number of data entries was oversampled proportionally before training. 90% of the augmented and oversampled data were used for training, with 10% for testing. Our team "Leicester Fox" achieved accuracy of 90.6%, AUROC of 0.745 and AUPRC of 0.079 in the test set A. The entry for the official phase of the PhysioNet/CinC 2019 competition received a normalized utility score of 0.237 on full data. Machine learning and neural networks approaches showed potential application for better prediction of sepsis, using real-world database with random missing data and imbalanced classes.

Convolutional and Recurrent Neural Networks for Early Detection of Sepsis Using Hourly Physiological Data from Patients in Intensive Care Unit