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. 2012 Apr 24;3 Suppl 1(Suppl 1):S5.
doi: 10.1186/2041-1480-3-S1-S5.

Annotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes

Paea Lependu  1 , Srinivasan V Iyer, Cédrick Fairon, Nigam H Shah
Affiliations

Annotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes

Paea Lependu et al. J Biomed Semantics. .

Abstract

Background: The electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data-in particular the clinical notes-it may be possible to computationally encode and to test drug safety signals in an active manner.

Results: We describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005.

Conclusions: Our results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records.

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Figures

Figure 1
Figure 1
The NCBO Annotator Workflow extracts terms from the clinical notes of patients: (1) We obtain a lexicon of over 2.8-million terms from the NCBO BioPortal library. (2) We use the NCBO Annotator to rapidly find those terms in clinical notes—which we call annotations. (3) We apply NegEx trigger rules to separate negated terms. (4) We compile terms (both positive and negative) into a temporally ordered series of sets for each patient and combine them with coded and structured data when possible. (5) We reason over the structure of the ontologies to normalize and to aggregate terms for further analysis.
Figure 2
Figure 2
The Vioxx risk pattern (top row) occurs when a patient with rheumatoid arthritis (RA) who takes Vioxx and suffers a myocardial infarction (MI)—these frequencies are entered into cell a of the 2x2 contingency matrix. Based on the various combinations of temporal orderings for the initial mentions of RA, Vioxx, and MI in the patient notes, other possible patterns contribute to the expected background distribution (cells b, c, d of the contingency table) that the odds ratio calculation requires.
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