Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers


We derived a testing dataset of 124 influenza patients and 87 non-influenza (shigellosis) patients. To assess NLP finding-extraction performance, we measured the overall accuracy, recall, and precision of Topaz and MedLEE parsers for 31 influenza-related findings against a reference standard established by three physician reviewers. To elucidate the relative contribution of NLP and BN classifier to classification performance, we compared the discriminative ability of nine combinations of finding-extraction methods (expert, Topaz, and MedLEE) and classifiers (one human-parameterized BN and two machine-parameterized BNs). To assess the effects of feature selection, we conducted secondary analyses of discriminative ability using the most influential findings defined by their likelihood ratios.

The overall accuracy of Topaz was significantly better than MedLEE (with post-processing) (0.78 vs 0.71, p0.70, p<0.05).

Using a three-component evaluation method we demonstrated how one could elucidate the relative contributions of components under an integrated framework. To improve classification performance, this study encourages researchers to improve NLP accuracy, use a machine-parameterized classifier, and apply feature selection methods.

To evaluate factors affecting performance of influenza detection, including accuracy of natural language processing (NLP), discriminative ability of Bayesian network (BN) classifiers, and feature selection.

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