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. 2017 Feb:66:72-81.
doi: 10.1016/j.jbi.2016.12.005. Epub 2016 Dec 16.

Accuracy of an automated knowledge base for identifying drug adverse reactions

E A Voss  1 , R D Boyce  2 , P B Ryan  3 , J van der Lei  4 , P R Rijnbeek  4 , M J Schuemie  5
Affiliations

Accuracy of an automated knowledge base for identifying drug adverse reactions

E A Voss et al. J Biomed Inform. 2017 Feb.

Abstract

Introduction: Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disseminate medical product safety evidence including spontaneous reports, published peer-reviewed literature, and product labels. Automated data processing and classification using these evidence sources can greatly reduce the manual curation currently required to develop reference sets of positive and negative controls (i.e. drugs that cause adverse drug events and those that do not) to be used in drug safety research.

Methods: In this paper we explore a method for automatically aggregating disparate sources of information together into a single repository, developing a predictive model to classify drug-adverse event relationships, and applying those predictions to a real world problem of identifying negative controls for statistical method calibration.

Results: Our results showed high predictive accuracy for the models combining all available evidence, with an area under the receiver-operator curve of ⩾0.92 when tested on three manually generated lists of drugs and conditions that are known to either have or not have an association with an adverse drug event.

Conclusions: Results from a pilot implementation of the method suggests that it is feasible to develop a scalable alternative to the time-and-resource-intensive, manual curation exercise previously applied to develop reference sets of positive and negative controls to be used in drug safety research.

Keywords: Adverse drug reaction; Health outcome; Knowledge base; Machine-learning experiment; Pharmacovigilance.

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Figures

Figure 1
Figure 1
Graphical depiction of how the reference sets are used and how the models were training and tested. Leave-pair-out cross validation was used to evaluate the models built independently on EU-ADR and OMOP reference sets. The third model was trained on the combination of both the EU-ADR and OMOP reference sets and then tested using the AZCERT as the test set.
Figure 2
Figure 2
Histograms of predicted probabilities with AUCs for positive and negative controls in the various reference sets, using the model trained on both OMOP and EU-ADR Reference Set. Values: AUC (Lower Bound AUC-Upper Bound AUC) *calculated by assuming a 1% of the negative controls were misclassified. OMOP: Observational Medical Outcomes Partnership, EU-ADR: Exploring and Understanding Adverse Drug Reactions, AUC: area under the curve
See this image and copyright information in PMC

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