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

Ye Y, Tsui FR, Wagner M, Espino JU, Li Q.  Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers.  J Am Med Inform Assoc. 2014 Sept:21(5):815-823. (2014 Jan 9. doi: 10.1136/amiajnl-2013-001934. Epub ahead of print. PubMed PMID: 24406261.  PMCID:PMC in Process: Available on 09/01/2015.

OBJECTIVES:

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.

METHODS:

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.

RESULTS:

The overall accuracy of Topaz was significantly better than MedLEE (with post-processing) (0.78 vs 0.71, p<0.0001). Classifiers using human-annotated findings were superior to classifiers using Topaz/MedLEE-extracted findings (average area under the receiver operating characteristic (AUROC): 0.75 vs 0.68, p=0.0113), and machine-parameterized classifiers were superior to the human-parameterized classifier (average AUROC: 0.73 vs 0.66, p=0.0059). The classifiers using the 17 'most influential' findings were more accurate than classifiers using all 31 subject-matter expert-identified findings (average AUROC: 0.76>0.70, p<0.05).

CONCLUSIONS:

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.

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Publication Year: 
2014
Publication Credits: 
Ye Y, Tsui FR, Wagner M, Espino JU, Li Q
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