The contribution of the Vaccine adverse event Text Mining system to the classification of possible Guillain-Barré Syndrome reports

Summary Background: We previously demonstrated that a general purpose text mining system, the Vaccine a dverse e vent Text Mining (V ae TM) system, could be used to automatically classify reports of anaphylaxis for post-marketing safety surveillance of vaccines. Objective: To evaluate the ability of V ae TM to classify reports to the Vaccine Adverse Event Reporting System (VAERS) of possible Guillain-Barré Syndrome (GBS). Methods: We used V ae TM to extract the key diagnostic features from the text of reports in VAERS. Then, we applied the Brighton Collaboration (BC) case definition for GBS, and an information retrieval strategy (i.e. the vector space model) to quantify the specific information that is included in the key features extracted by V ae TM and compared it with the encoded information that is already stored in VAERS as Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs). We also evaluated the contribution of the primary (diagnosis and cause of death) and secondary (second level diagnosis and symptoms) diagnostic V ae TM-based features to the total V ae TM-based information. Results: MedDRA captured more information and better supported the classification of reports for GBS than V ae TM (AUC: 0.904 vs. 0.777); the lower performance of V ae TM is likely due to the lack of extraction by V ae TM of specific laboratory results that are included in the BC criteria for GBS. On the other hand, the V ae TM-based classification exhibited greater specificity than the MedDRA-based approach (94.96% vs. 87.65%). Most of the V ae TM-based information was contained in the secondary diagnostic features. Conclusion: For GBS, clinical signs and symptoms alone are not sufficient to match MedDRA coding for purposes of case classification, but are preferred if specificity is the priority. Citation: Botsis T, Woo EJ, Ball R. The contribution of the vaccine adverse event text mining system to the classification of possible Guillain-Barré syndrome reports. Appl Clin Inf 2013; 4: 88–99 http://dx.doi.org/10.4338/ACI-2012-11-RA-0049