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    AMIA Annu Symp Proc. 2008 Nov 6:207-11.

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

    Friedlin J, Grannis S, Overhage JM.

    Regenstrief Institute, Inc, Indianapolis, IN, USA.

    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.

    PMID: 18999177 [PubMed - in process]

    PMCID: PMC2656046

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