Intensive Care Unit readmission prediction with correlation enhanced multi-task learning

Niu, K; Pei, S; Peng, X; Zeng, J; Zhang, K

Intensive Care Unit readmission prediction with correlation enhanced multi-task learning

Niu, K Pei, S Peng, X

Zeng, J Zhang, K

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Computers and Electrical Engineering, 2023, 110, pp. 108780
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Niu, K
dc.contributor.author	Pei, S
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472
dc.contributor.author	Zeng, J
dc.contributor.author	Zhang, K
dc.date.accessioned	2023-09-13T01:37:11Z
dc.date.available	2023-09-13T01:37:11Z
dc.date.issued	2023-09-01
dc.identifier.citation	Computers and Electrical Engineering, 2023, 110, pp. 108780
dc.identifier.issn	0045-7906
dc.identifier.uri	http://hdl.handle.net/10453/172073
dc.description.abstract	Prediction for Intensive Care Unit (ICU) readmission is conducive to assisting doctors in treatment-related decision making and reducing the risk of relapse after discharge. Recently, existing ICU readmission prediction approaches train each sub-task independently, which prevents the models from using complementary information between these sub-tasks. In this paper, we propose correlation enhanced Multi-Task learning with Pearson and RNN-based Neural Ordinary Differential Equations Model (MP-ROM). In order to enhance the learning of general features and avoid the local optima in single-task training, we construct the Shared-Bottom structure of multi-task learning, which enables multiple tasks to share model structure and parameters. Besides, we add the task correlation score calculated by Pearson correlation calculation, enhancing the association between sub-tasks. Experiment results on MIMIC-III dataset show that MP-ROM achieves the highest average precision and demonstrates that task association enhanced can further improve the predictive performance of ICU readmission risk.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Computers and Electrical Engineering
dc.relation.isbasedon	10.1016/j.compeleceng.2023.108780
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Intensive Care Unit readmission prediction with correlation enhanced multi-task learning
dc.type	Journal Article
utslib.citation.volume	110
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	open_access	*
dc.date.updated	2023-09-13T01:37:10Z
pubs.publication-status	Accepted
pubs.volume	110

Abstract:

Prediction for Intensive Care Unit (ICU) readmission is conducive to assisting doctors in treatment-related decision making and reducing the risk of relapse after discharge. Recently, existing ICU readmission prediction approaches train each sub-task independently, which prevents the models from using complementary information between these sub-tasks. In this paper, we propose correlation enhanced Multi-Task learning with Pearson and RNN-based Neural Ordinary Differential Equations Model (MP-ROM). In order to enhance the learning of general features and avoid the local optima in single-task training, we construct the Shared-Bottom structure of multi-task learning, which enables multiple tasks to share model structure and parameters. Besides, we add the task correlation score calculated by Pearson correlation calculation, enhancing the association between sub-tasks. Experiment results on MIMIC-III dataset show that MP-ROM achieves the highest average precision and demonstrates that task association enhanced can further improve the predictive performance of ICU readmission risk.

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http://hdl.handle.net/10453/172073