Sepsis Detection in Sparse Clinical Data Using Long Short-Term Memory Network with Dice Loss

This paper aims to present a methodology for sepsis prediction from clinical time-series data. Sepsis is one of the most threatening states which could occur while treating a patient at the intensive care unit. Therefore its prediction could significantly improve the quality of the patient treatment.In this work, we address the problem ofsepsis prediction with Long Short-Term Memory (LSTM) network with specialized deep architecture with residual connections. The output of the network is sepsis prediction score at each point in time.Feature normalization into the fixed range of values is applied including replacing missing values with numerical representation from outside the normalized range. Therefore, the LSTM network is able to include missing values in the learning process. Also, the rarity of sepsis occurrence in the provided dataset is a challenging problem. This problem is addressed by the application of dice loss providing automatically weighted classes by the occurrence of the feature.The proposed method leads to 0.281 normalized utility score on the full test set as the best official Phys- ioNet/Computing in Cardiology (CinC) Challenge 2019 entry ofECGuru10 team.