Using natural language processing to improve accuracy of automated notifiable disease reporting.

Jeff Friedlin, Shaun Grannis, J. Marc Overhage

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)207-211
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2008

ASJC Scopus subject areas

  • Medicine(all)

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