Sheikhalishahi, S., Balaraman, V. and Osmani, V. orcid.org/0000-0001-7306-2972 (2020) Benchmarking machine learning models on multi-centre eICU critical care dataset. PLoS ONE, 15 (7). e0235424. ISSN 1932-6203
Abstract
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabled the possibility of establishing public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep learning models using eICU critical care dataset of around 73,000 patients. This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model. We also investigate the impact of numerical variables as well as handling of categorical variables on each of the defined tasks. The source code, detailing our methods and experiments is publicly available such that anyone can replicate our results and build upon our work.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 Sheikhalishahi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Machine learning; Intensive care units; Deep learning; Hospitals; Artificial neural networks; Medical risk factors; Forecasting; Phenotypes |
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: | 16 Nov 2022 12:26 |
Last Modified: | 16 Nov 2022 12:26 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pone.0235424 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193447 |
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