J Am Med Inform Assoc - Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection.

Tópicos

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Resumo

JECTIVE: 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.DESIGN: 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.MEASUREMENTS: Classifiers' performance was evaluated by macro-averaging recall, precision, and F-measure, and Friedman's test; misclassification error rate analysis was also performed.RESULTS: 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).CONCLUSION: 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.

Resumo Limpo

jectiv us vaccin advers event report system vaer collect spontan report advers event follow vaccin medic offic review report often appli standard case definit develop brighton collabor object demonstr multilevel text mine approach autom text classif vaer report potenti reduc human workloaddesign select vaer report hn vaccin classifi medic offic potenti posit npos negat anaphylaxi creat categor corpus text file includ class label symptom text field report valid set label text file also use text mine techniqu appli extract three featur set import keyword low highlevel pattern rulebas classifi process highlevel featur represent sever machin learn classifi train remain two featur representationsmeasur classifi perform evalu macroaverag recal precis fmeasur friedman test misclassif error rate analysi also performedresult rulebas classifi boost tree weight support vector machin perform well term macrorecal howev expens higher mean misclassif error rate rulebas classifi perform well term averag sensit specif respectivelyconclus valid result show possibl develop effect medic text classifi vaer report combin text mine inform featur select strategi potenti reduc review workload consider

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