Unsupervised learning approach for predicting sepsis onset in ICU patients

Guilherme Ramos, Erida Gjini, Luis Coelho, Margarida Silveira

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1916-1919
Number of pages4
ISBN (Electronic)9781728111797
DOIs
Publication statusPublished - 2021
Event43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico
Duration: 1 Nov 20215 Nov 2021

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Country/TerritoryMexico
CityVirtual, Online
Period1/11/215/11/21

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