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Identifying plausible adverse drug reactions using knowledge extracted from the literature
- Source :
- Journal of Biomedical Informatics. 52:293-310
- Publication Year :
- 2014
- Publisher :
- Elsevier BV, 2014.
-
Abstract
- Display Omitted We evaluate PSI-based identification of relationships between drugs and ADRs.PSI can incorporate relations and concepts and infer reasoning pathways.PSI is more predictive than a co-occurrence based model.Reasoning pathways suggest supporting evidence from literature for review. Pharmacovigilance involves continually monitoring drug safety after drugs are put to market. To aid this process; algorithms for the identification of strongly correlated drug/adverse drug reaction (ADR) pairs from data sources such as adverse event reporting systems or Electronic Health Records have been developed. These methods are generally statistical in nature, and do not draw upon the large volumes of knowledge embedded in the biomedical literature. In this paper, we investigate the ability of scalable Literature Based Discovery (LBD) methods to identify side effects of pharmaceutical agents. The advantage of LBD methods is that they can provide evidence from the literature to support the plausibility of a drug/ADR association, thereby assisting human review to validate the signal, which is an essential component of pharmacovigilance. To do so, we draw upon vast repositories of knowledge that has been extracted from the biomedical literature by two Natural Language Processing tools, MetaMap and SemRep. We evaluate two LBD methods that scale comfortably to the volume of knowledge available in these repositories. Specifically, we evaluate Reflective Random Indexing (RRI), a model based on concept-level co-occurrence, and Predication-based Semantic Indexing (PSI), a model that encodes the nature of the relationship between concepts to support reasoning analogically about drug-effect relationships. An evaluation set was constructed from the Side Effect Resource 2 (SIDER2), which contains known drug/ADR relations, and models were evaluated for their ability to "rediscover" these relations. In this paper, we demonstrate that both RRI and PSI can recover known drug-adverse event associations. However, PSI performed better overall, and has the additional advantage of being able to recover the literature underlying the reasoning pathways it used to make its predictions.
- Subjects :
- Biomedical Research
Drug-Related Side Effects and Adverse Reactions
Computer science
MEDLINE
Literature-based discovery
Health Informatics
Semantics
Article
Pharmacovigilance
03 medical and health sciences
0302 clinical medicine
medicine
Adverse Drug Reaction Reporting Systems
Data Mining
Humans
030212 general & internal medicine
Set (psychology)
Natural Language Processing
030304 developmental biology
Distributional semantics
0303 health sciences
Information retrieval
Predication-based semantic indexing
Reflective random indexing
medicine.disease
Data science
Computer Science Applications
Random indexing
Identification (information)
ROC Curve
Algorithms
Adverse drug reaction
Subjects
Details
- ISSN :
- 15320464
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- Journal of Biomedical Informatics
- Accession number :
- edsair.doi.dedup.....931d8281d39ef3f2d0793f68908e60a5