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Using Natural Language Processing to Improve Accuracy of Automated Notifiable Disease Reporting

By Jeff Friedlin, Shaun Grannis and J. Marc Overhage

Abstract

We examined whether using a natural language processing (NLP) system results in improved accuracy and completeness of automated electronic laboratory reporting (ELR) of notifiable conditions. We used data from a community-wide health information exchange that has automated ELR functionality. We focused on methicillin-resistant Staphylococcus Aureus (MRSA), a reportable infection found in unstructured, free-text culture result reports. We used the Regenstrief EXtraction tool (REX) for this work. REX processed 64,554 reports that mentioned MRSA and we compared its output to a gold standard (human review). REX correctly identified 39,491(99.96%) of the 39,508 reports positive for MRSA, and committed only 74 false positive errors. It achieved high sensitivity, specificity, positive predicted value and F-measure. REX identified over two times as many MRSA positive reports as the ELR system without NLP. Using NLP can improve the completeness and accuracy of automated ELR

Topics: Articles
Publisher: American Medical Informatics Association
OAI identifier: oai:pubmedcentral.nih.gov:2656046
Provided by: PubMed Central
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