Article

Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection

Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, Maryland 20852, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.5). 06/2011; 18(5):631-8. DOI: 10.1136/amiajnl-2010-000022
Source: PubMed

ABSTRACT

The US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.
We selected 6034 VAERS reports for H1N1 vaccine that were classified by medical officers as potentially positive (N(pos)=237) or negative for anaphylaxis. We created a categorized corpus of text files that included the class label and the symptom text field of each report. A validation set of 1100 labeled text files was also used. Text mining techniques were applied to extract three feature sets for important keywords, low- and high-level patterns. A rule-based classifier processed the high-level feature representation, while several machine learning classifiers were trained for the remaining two feature representations.
Classifiers' performance was evaluated by macro-averaging recall, precision, and F-measure, and Friedman's test; misclassification error rate analysis was also performed.
Rule-based classifier, boosted trees, and weighted support vector machines performed well in terms of macro-recall, however at the expense of a higher mean misclassification error rate. The rule-based classifier performed very well in terms of average sensitivity and specificity (79.05% and 94.80%, respectively).
Our validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.

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    • "In human medicine, text mining has been successfully applied to clinical records in many public health surveillance systems (Botsis et al., 2011; Steinberger et al., 2008; Brownstein et al., 2008; Wagner et al., 2004). The approaches range from hand-written rule-based systems to fully automated methods using machine learning. "
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    ABSTRACT: Public health surveillance systems rely on the automated monitoring of large amounts of text. While building a text mining system for veterinary syndromic surveillance, we exploit automatic and semi-automatic methods for terminology construction at different stages. Our approaches include term extraction from free-text, grouping of term variants based on string similarity, and linking to an existing medical ontology.
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    • "Weights can also be applied to the variables. The Brighton Collaboration case definition has been implemented as a rule-based program and using features representing the clinical concepts extracted from narrative case descriptions, this program has worked reasonably well to classify cases [46] "
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    ABSTRACT: Safety of medical products is a major public health concern. We present a critical discussion of the currently used analytical tools for mining spontaneous reporting systems (SRS) to identify safety signals after use of medical products. We introduce a pattern discovery framework for the analysis of SRS. The terminology ‘pattern discovery’ is borrowed from the engineering and artificial intelligence literature and signifies that the basis of the proposed framework is the medical case, formalizing the cognitive paradigm known to clinicians who evaluate individual patients and individual case safety reports submitted to SRS. The fundamental contribution of this approach is a strong probabilistic component that may account for selection and other biases and facilitates rigorous modeling and inference. We discuss somewhat in depth the concept of signal in pharmacovigilance and connect it with the concept of a pattern; we illustrate this conceptual framework using the example of anaphylaxis. Finally, we propose a research agenda in statistics, informatics, and pharmacovigilance practices needed to advance the pattern discovery framework in both the short and long terms.
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    • "Analysis of biomedical literature for safety signal detection is challenging and labor intensive due to unstructured nature. Therefore, natural-language processing (NLP) techniques recently developed for extracting ADE-related information or direct/indirect drug interactions have gained large popularity[35]-[37]. "

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