Hybrid Feature Learning Using Autoencoders for Early Prediction of Sepsis

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.

[1]  Mehul Motani,et al.  Deep Spatio-Temporal Feature Learning using Autoencoders , 2018 .

[2]  Shamim Nemati,et al.  Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019 , 2019, 2019 Computing in Cardiology (CinC).

[3]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[4]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[5]  Mehul Motani,et al.  SURI: Feature Selection Based on Unique Relevant Information for Health Data , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[6]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[7]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[10]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[11]  Viorica Patraucean,et al.  Spatio-temporal video autoencoder with differentiable memory , 2015, ArXiv.

[12]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[13]  Shamim Nemati,et al.  Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019 , 2019, 2019 Computing in Cardiology (CinC).

[14]  William Fleischman,et al.  Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach. , 2016, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[15]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[16]  Uli K. Chettipally,et al.  Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach , 2016, JMIR medical informatics.