Appl Clin Inform 2013; 04(01): 88-99
DOI: 10.4338/ACI-2012-11-RA-0049
Research Article
Schattauer GmbH

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

T. Botsis
1  Food and Drug Administration, Center for Biologics Evaluation and Research, Rockville, Maryland, United States
2  Department of Computer Science, University of Tromsø, Norway
,
E. J. Woo
1  Food and Drug Administration, Center for Biologics Evaluation and Research, Rockville, Maryland, United States
,
R. Ball
1  Food and Drug Administration, Center for Biologics Evaluation and Research, Rockville, Maryland, United States
› Author Affiliations
Further Information

Correspondence to:

Taxiarchis Botsis, PhD, MS
Office of Biostatistics & Epidemiology
Center for Biologics Evaluation and Research (CBER),
Food and Drug Administration (FDA)
Woodmont Office Complex 1, Rm 306N
1401 Rockville Pike
Rockville, MD 20852
Phone: +1 301–827–5405   

Publication History

received: 14 November 2012

accepted: 01 February 2013

Publication Date:
19 December 2017 (online)

 

Summary

Background: We previously demonstrated that a general purpose text mining system, the Vaccine adverse event Text Mining (VaeTM) system, could be used to automatically classify reports of anaphylaxis for post-marketing safety surveillance of vaccines.

Objective: To evaluate the ability of VaeTM to classify reports to the Vaccine Adverse Event Reporting System (VAERS) of possible Guillain-Barré Syndrome (GBS).

Methods: We used VaeTM 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 VaeTM 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 VaeTM-based features to the total VaeTM-based information.

Results: MedDRA captured more information and better supported the classification of reports for GBS than VaeTM (AUC: 0.904 vs. 0.777); the lower performance of VaeTM is likely due to the lack of extraction by VaeTM of specific laboratory results that are included in the BC criteria for GBS. On the other hand, the VaeTM-based classification exhibited greater specificity than the MedDRA-based approach (94.96% vs. 87.65%). Most of the VaeTM-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


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Conflicts of Interest

The authors declare that they have no conflicts of interest in the research.


Correspondence to:

Taxiarchis Botsis, PhD, MS
Office of Biostatistics & Epidemiology
Center for Biologics Evaluation and Research (CBER),
Food and Drug Administration (FDA)
Woodmont Office Complex 1, Rm 306N
1401 Rockville Pike
Rockville, MD 20852
Phone: +1 301–827–5405