A case study on sepsis using PubMed and Deep Learning for ontology learning

Arguello Casteleiro, Mercedes, Maseda Fernandez, Diego, Demetriou, George, Read, Warren, Fernandez-Prieto, MJ, Des Diz, Julio, Nenadic, Goran, Keane, John and Stevens, Robert 2017, 'A case study on sepsis using PubMed and Deep Learning for ontology learning' , Informatics for Health: Connected Citizen-Led Wellness and Population Health, 235 , 516 -520.

Preview	PDF - Published Version Available under License Creative Commons Attribution Non-commercial 4.0. Download (260kB) | Preview
	PDF Download (74kB)

Abstract

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision.

Item Type:	Article
Additional Information:	Editors: Rebecca Randell, Ronald Cornet, Colin McCowan, Niels Peek, Philip J. Scott Series: Studies in Health Technology and Informatics
Schools:	Schools > School of Humanities, Languages & Social Sciences > Centre for Linguistics and Applied Linguistics
Journal or Publication Title:	Informatics for Health: Connected Citizen-Led Wellness and Population Health
Publisher:	IOS Press Ebooks
ISSN:	9781614997528
Related URLs:	Publication
Depositing User:	MJ Fernandez-Prieto
Date Deposited:	03 May 2017 14:13
Last Modified:	08 May 2019 15:46
URI:	http://usir.salford.ac.uk/id/eprint/42259

Actions (login required)

Edit record (repository staff only)