Wernly, B., Mamandipoor, B., Baldia, P. et al. (2 more authors) (2021) Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation. International Journal of Medical Informatics, 145. 104312. ISSN 1386-5056
Abstract
Purpose
: To evaluate the application of machine learning methods, specifically Deep Neural Networks (DNN) models for intensive care (ICU) mortality prediction. The aim was to predict mortality within 96 hours after admission to mirror the clinical situation of patient evaluation after an ICU trial, which consists of 24-48 hours of ICU treatment and then “re-triage”. The input variables were deliberately restricted to ABG values to maximise real-world practicability.
Methods
: We retrospectively evaluated septic patients in the multi-centre eICU dataset as well as single centre MIMIC-III dataset. Included were all patients alive after 48 hours with available data on ABG (n = 3979 and n = 9655 ICU stays for the multi-centre and single centre respectively). The primary endpoint was 96 -h-mortality.
Results
: The model was developed using long short-term memory (LSTM), a type of DNN designed to learn temporal dependencies between variables. Input variables were all ABG values within the first 48 hours. The SOFA score (AUC of 0.72) was moderately predictive. Logistic regression showed good performance (AUC of 0.82). The best performance was achieved by the LSTM-based model with AUC of 0.88 in the multi-centre study and AUC of 0.85 in the single centre study.
Conclusions
: An LSTM-based model could help physicians with the “re-triage” and the decision to restrict treatment in patients with a poor prognosis.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier B.V. This is an author produced version of a paper subsequently published in International Journal of Medical Informatics. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | critically ill; artificial intelligence; machine learning; deep learning; LSTM; ICU; risk stratification; intensive care unit; critical care; sepsis |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Nov 2022 11:06 |
Last Modified: | 29 Nov 2022 15:58 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.ijmedinf.2020.104312 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193813 |