Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Research article

Knowledge-based extraction of adverse drug events from biomedical text

Ning Kang*, Bharat Singh, Chinh Bui, Zubair Afzal, Erik M van Mulligen and Jan A Kors

Author Affiliations

Department of Medical Informatics, Erasmus University Medical Center, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands

For all author emails, please log on.

BMC Bioinformatics 2014, 15:64  doi:10.1186/1471-2105-15-64

Published: 4 March 2014

Abstract

Background

Many biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The system consists of a concept recognition module that identifies drugs and adverse effects in sentences, and a knowledge-base module that establishes whether a relation exists between the recognized concepts. The knowledge base was filled with information from the Unified Medical Language System. The performance of the system was evaluated on the ADE corpus, consisting of 1644 abstracts with manually annotated adverse drug events. Fifty abstracts were used for training, the remaining abstracts were used for testing.

Results

The knowledge-based system obtained an F-score of 50.5%, which was 34.4 percentage points better than the co-occurrence baseline. Increasing the training set to 400 abstracts improved the F-score to 54.3%. When the system was compared with a machine-learning system, jSRE, on a subset of the sentences in the ADE corpus, our knowledge-based system achieved an F-score that is 7 percentage points higher than the F-score of jSRE trained on 50 abstracts, and still 2 percentage points higher than jSRE trained on 90% of the corpus.

Conclusion

A knowledge-based approach can be successfully used to extract adverse drug events from biomedical text without need for a large training set. Whether use of a knowledge base is equally advantageous for other biomedical relation-extraction tasks remains to be investigated.

Keywords:
Relation extraction; Knowledge base; Adverse drug effect