Automatic integration of drug indications from multiple health resources

Authors:
Aurélie Névéol;Zhiyong Lu
Affiliations:
U.S. National Library of Medicine, Bethesda, MD, USA;U.S. National Library of Medicine, Bethesda, MD, USA
Venue:
Proceedings of the 1st ACM International Health Informatics Symposium
Year:
2010

Citing 4
Cited 3

The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text

Journal of Biomedical Informatics - Special issue: Unified medical language system
Classifying semantic relations in bioscience texts

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Collaborative text-annotation resource for disease-centered relation extraction from biomedical text

Journal of Biomedical Informatics
Automatic summarization of MEDLINE citations for evidence-based medical treatment: A topic-oriented evaluation

Journal of Biomedical Informatics

Sentiment lexicons for health-related opinion mining

Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Semantics-aware open information extraction in the biomedical domain

Proceedings of the 4th International Workshop on Semantic Web Applications and Tools for the Life Sciences
Cross-domain targeted ontology subsets for annotation: The case of SNOMED CORE and RxNorm

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Drug indication refers to what disease(s) a drug may treat -- a type of information that is frequently sought by biomedical researchers, health care professionals and the general public. Although such information may be available online, it is often challenging for non-experts to glean unbiased reliable information from multiple websites of various quality. In addition, most drug indication information is only available in free text as opposed to structured format, thus making it difficult for further automatic analysis by computers. In response, we herein focus on automatically extracting and integrating drug indication information from multiple resources such as DailyMed and MeSH Scope notes. We select trustworthy resources of drug/disease relationships and apply state-of-the-art relationship extraction methods, customized to improve recall and perform ellipsis and anaphora resolution. As a result, 7,670 unique TREATS relationships between 4,666 drugs and 1,293 diseases are integrated from 4 different sources with an estimated overall correctness of 77% and specificity of 84%.