title: Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers.
abstract: OBJECTIVE
  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.
authors.affiliation: Real-time Outbreak and Disease Surveillance Laboratory (RODS), Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Ye
authors.initials: Y
authors.lastname: Ye
authors.affiliation: Real-time Outbreak and Disease Surveillance Laboratory (RODS), Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Fuchiang Rich
authors.initials: FR
authors.lastname: Tsui
authors.affiliation: Real-time Outbreak and Disease Surveillance Laboratory (RODS), Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Wagner
authors.affiliation: Real-time Outbreak and Disease Surveillance Laboratory (RODS), Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Jeremy U
authors.initials: JU
authors.lastname: Espino
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004405:Dysentery, Bacillary; D057286:Electronic Health Records; D004636:Emergency Service, Hospital; D006801:Humans; D007251:Influenza, Human; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing