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; Stevens, Robert

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.

• Journal Article Type: Article
• Deposit Date: May 3, 2017
• Publicly Available Date: May 3, 2017
• Journal: Informatics for Health: Connected Citizen-Led Wellness and Population Health
• Print ISSN: 9781614997528
• Electronic ISSN: 9781614997535
• Volume: 235
• Pages: 516 -520
• DOI: https://doi.org/10.3233/978-1-61499-753-5-516
• Publisher URL: http://ebooks.iospress.nl/publication/46394
• Related Public URLs: http://ebooks.iospress.nl/volume/informatics-for-health-connected-citizen-led-wellness-and-population-health
• Additional Information: Additional Information : Editors: Rebecca Randell, Ronald Cornet, Colin McCowan, Niels Peek, Philip J. Scott Series: Studies in Health Technology and Informatics