Hybrid Feature Learning Using Autoencoders for Early Prediction of Sepsis
Jia Yao, Ming Lun Ong, Kar Kin Mun, Shiyu Liu, Mehul Motani · Computing in cardiology · 2019
The early prediction of sepsis is important for ICU patients, as the risk of mortality increases as the disease is left untreated.We hypothesize that there is a need to learn important feature representations, such as to extract salient information from sepsis data.In this paper, we propose an unsupervised method to learn spatial-temporal information from the data, through the use of two autoencoders.For the official 2019 PhysioNet Challenge, our team, Kent Ridge AI (ranked 77th), obtained a utility score of -0.164 on the full test set.Additionally, we report crossvalidation results and identify several issues which can potentially help to improve performance.