Fusion of sequential visits and medical ontology for mortality prediction.

Niu, K; Lu, Y; Peng, X; Zeng, J

Fusion of sequential visits and medical ontology for mortality prediction.

Niu, K Lu, Y Peng, X

Zeng, J

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Publisher:: ACADEMIC PRESS INC ELSEVIER SCIENCE
Publication Type:: Journal Article
Citation:: J Biomed Inform, 2022, 127, pp. 104012
Issue Date:: 2022-03

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Field	Value	Language
dc.contributor.author	Niu, K
dc.contributor.author	Lu, Y
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472
dc.contributor.author	Zeng, J
dc.date.accessioned	2022-05-26T06:10:42Z
dc.date.available	2022-02-02
dc.date.available	2022-05-26T06:10:42Z
dc.date.issued	2022-03
dc.identifier.citation	J Biomed Inform, 2022, 127, pp. 104012
dc.identifier.issn	1532-0464
dc.identifier.issn	1532-0480
dc.identifier.uri	http://hdl.handle.net/10453/157724
dc.description.abstract	The goal of mortality prediction task is to predict the future death risk of patients according to their previous Electronic Healthcare Records (EHR). The main challenge of mortality prediction is how to design an accurate and robust predictive model with sequential, multivariate, sparse and irregular EHR data. In addition, the performance of model may be affected by lack of sufficient information of some patients with rare diseases in EHRs. To address these challenges, we propose a model to fuse Sequential visits and Medical Ontology to predict patients' death risk. SeMO not only learns reasonable embeddings for medical concepts from sequential and irregular visits, but also exploits medical ontology to improve the prediction performance. With integration of multivariate features, SeMO learns robust representations of medical codes, mitigating data insufficiency and insightful sequential dependencies among patient's visits. Experimental results on real world datasets prove that the proposed SeMO improves the prediction performance compared with the baseline approaches. Our model achieves an precision of up to 0.975. Compared with RNN, the precision has been improved up to 2.204%.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ACADEMIC PRESS INC ELSEVIER SCIENCE
dc.relation.ispartof	J Biomed Inform
dc.relation.isbasedon	10.1016/j.jbi.2022.104012
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	06 Biological Sciences, 08 Information and Computing Sciences, 11 Medical and Health Sciences
dc.subject.classification	Biomedical Engineering
dc.subject.classification	Medical Informatics
dc.subject.mesh	Electronic Health Records
dc.subject.mesh	Forecasting
dc.subject.mesh	Humans
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Humans
dc.subject.mesh	Forecasting
dc.subject.mesh	Electronic Health Records
dc.subject.mesh	Neural Networks, Computer
dc.title	Fusion of sequential visits and medical ontology for mortality prediction.
dc.type	Journal Article
utslib.citation.volume	127
utslib.location.activity	United States
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	11 Medical and Health Sciences
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	closed_access	*
dc.date.updated	2022-05-26T06:10:41Z
pubs.publication-status	Published
pubs.volume	127

Abstract:

The goal of mortality prediction task is to predict the future death risk of patients according to their previous Electronic Healthcare Records (EHR). The main challenge of mortality prediction is how to design an accurate and robust predictive model with sequential, multivariate, sparse and irregular EHR data. In addition, the performance of model may be affected by lack of sufficient information of some patients with rare diseases in EHRs. To address these challenges, we propose a model to fuse Sequential visits and Medical Ontology to predict patients' death risk. SeMO not only learns reasonable embeddings for medical concepts from sequential and irregular visits, but also exploits medical ontology to improve the prediction performance. With integration of multivariate features, SeMO learns robust representations of medical codes, mitigating data insufficiency and insightful sequential dependencies among patient's visits. Experimental results on real world datasets prove that the proposed SeMO improves the prediction performance compared with the baseline approaches. Our model achieves an precision of up to 0.975. Compared with RNN, the precision has been improved up to 2.204%.

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http://hdl.handle.net/10453/157724