Readmission Prediction with Knowledge Graph Attention and RNN-Based Ordinary Differential Equations

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

Readmission Prediction with Knowledge Graph Attention and RNN-Based Ordinary Differential Equations

Pei, S Niu, K Peng, X

Zeng, J

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, 12817 LNAI, pp. 559-570
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Pei, S
dc.contributor.author	Niu, K
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472
dc.contributor.author	Zeng, J
dc.date.accessioned	2021-12-14T23:32:49Z
dc.date.available	2021-12-14T23:32:49Z
dc.date.issued	2021-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, 12817 LNAI, pp. 559-570
dc.identifier.isbn	9783030821524
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/152335
dc.description.abstract	Predicting the readmission risk within 30 days on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in healthcare domain. Deep-learning-based models are recently utilized to address this task since those models can relatively improve prediction performance and work as decision aids, which helps reduce unnecessary readmission and recurrence risk. However, existing prediction models, limited by fuzzy relevance of patient data, are unable to get higher prediction accuracy due to data noise generated by patients with different disease types. To solve this problem, we propose an end-to-end model called GROM, which integrates knowledge graph to alleviate the interference of data noise generated in the processing of irregularity dynamic clinical data with neural ordinary differential equation (ODE). The experimental results show that our model achieved the highest average precision and proved that the graph attention mechanism is suitable to improve performance of predicting the risk of readmission within 30 days.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-82153-1_46
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)]. The final authenticated version is available online at: https://link.springer.com/chapter/10.1007%2F978-3-030-82153-1_46”
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Readmission Prediction with Knowledge Graph Attention and RNN-Based Ordinary Differential Equations
dc.type	Conference Proceeding
utslib.citation.volume	12817 LNAI
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	recently_added	*
dc.date.updated	2021-12-14T23:32:47Z
pubs.publication-status	Published
pubs.volume	12817 LNAI

Abstract:

Predicting the readmission risk within 30 days on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in healthcare domain. Deep-learning-based models are recently utilized to address this task since those models can relatively improve prediction performance and work as decision aids, which helps reduce unnecessary readmission and recurrence risk. However, existing prediction models, limited by fuzzy relevance of patient data, are unable to get higher prediction accuracy due to data noise generated by patients with different disease types. To solve this problem, we propose an end-to-end model called GROM, which integrates knowledge graph to alleviate the interference of data noise generated in the processing of irregularity dynamic clinical data with neural ordinary differential equation (ODE). The experimental results show that our model achieved the highest average precision and proved that the graph attention mechanism is suitable to improve performance of predicting the risk of readmission within 30 days.

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http://hdl.handle.net/10453/152335