Automated Determination of Publications Related to Adverse Drug Reactions in PubMed.
Bottom Line: Timely dissemination of up-to-date information concerning adverse drug reactions (ADRs) at the point of care can significantly improve medication safety and prevent ADRs.Evaluation was performed on a limited sample, resulting in a sensitivity of 90% and precision of 93%.Results demonstrated that this method is highly effective.
Affiliation: Stony Brook University, Stony Brook, NY.
Timely dissemination of up-to-date information concerning adverse drug reactions (ADRs) at the point of care can significantly improve medication safety and prevent ADRs. Automated methods for finding relevant articles in MEDLINE which discuss ADRs for specific medications can facilitate decision making at the point of care. Previous work has focused on other types of clinical queries and on retrieval for specific ADRs or drug-ADR pairs, but little work has been published on finding ADR articles for a specific medication. We have developed a method to generate a PubMED query based on MESH, supplementary concepts, and textual terms for a particular medication. Evaluation was performed on a limited sample, resulting in a sensitivity of 90% and precision of 93%. Results demonstrated that this method is highly effective. Future work will integrate this method within an interface aimed at facilitating access to ADR information for specified drugs at the point of care.
No MeSH data available.
Related in: MedlinePlus
Mentions: A total of 1,644 unique PubMed identifiers (PMID’s) were obtained from an online corpus of adverse drug event abstracts (https://sites.google.com/site/adecorpus/home) and the corresponding abstracts were collected15. A statistical package called WordStat (http://provalisresearch.com/products/content-analysis-software/) was used to analyze properties of these abstracts, including high frequency text words and phrases, MeSH terms tagged as major and minor article topics, MeSH subheadings and supplementary concepts, which also contain names of drug and drug classes. High frequency text words and phrases were analyzed. The textual terms were found to be either too general to be useful for retrieving only adverse event articles, or were very specific for particular adverse events so that they were not generalizable. Minor MeSH terms were ignored since the topics they describe are not the main foci of the articles. Of the properties analyzed, high frequency major MeSH terms with subheadings were the most useful, since they describe the main focus of the article and cover a broad range of adverse events. Statistical analysis showed that a majority of the highest frequency major MeSH terms were Pharmacological Action (PA) terms with the “adverse event” subheading. This led to an initial base search method (“drugname”[TIAB] AND (“pharmacological action/adverse event” [MAJR]) which finds articles that have both the specified drugname in either the title or abstract and one of the drug’s corresponding PAs with the subheading “adverse event” as a major MeSH term. In order to obtain the appropriate PA(s) of a drug, the MeSH resource was used (http://www.nlm.nih.gov/bsd/disted/meshtutorial/pharmacologicalactionterms/) which assigns most substances to one or more PA terms. The additional subheadings “toxicity” and “poisoning” were added, after a review of MeSH subheadings, as they each denote an adverse event. An example of the initial search with two PA variables and all three subheadings is shown in Figure 1.
No MeSH data available.