Unsupervised learning approach for predicting sepsis onset in ICU patients.

Sepsis is a life-threatening condition caused by a deregulated host response to infection. If not diagnosed at an early stage, septic patients can go into a septic shock, associated with aggravated patient outcomes. Research has been mostly focused on predicting sepsis onset using supervised models that require big labeled datasets to train. In this work we propose two fully unsupervised learning approaches to predict septic shock onset in the Intensive Care Unit (ICU). Our approach includes learning representations from patient multivariate timeseries using Recurrent Autoencoders. Then, we apply an anomaly detection framework, using clustering-based algorithms, on the representation space learned by the models. When evaluating the performance of the proposed approaches in the septic shock onset prediction task, the Variational Autoencoder (VAE) using Gaussian Mixture Models in the anomaly detection framework was competitive with a supervised LSTM network. Results led to an AUC of 0.82 and F1-score of 0.65 using the unsupervised approach in comparison with 0.80, 0.66 for the supervised model.Clinical relevance- This work proposes an unsupervised septic shock onset prediction framework which can improve current procedure for monitoring infection progression in the ICU.