A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning
Arguello Casteleiro, M.; Maseda Fernandez, D.; Demetriou, G.; Read, W.; Fernandez Prieto, M.J.; Des Diz, J.; Nenadic, G.; Keane, J.; Stevens, R.
Studies in Health Technology and Informatics 235: 516-520
2017
ISSN/ISBN: 1879-8365 PMID: 28423846 Document Number: 692451
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.