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Representing information in patient reports using natural language processing and the extensible markup language.

C Friedman·G Hripcsak·L Shagina·H Liu
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Abstract

OBJECTIVE: To design a document model that provides reliable and efficient access to clinical information in patient reports for a broad range of clinical applications, and to implement an automated method using natural language processing that maps textual reports to a form consistent with the model. METHODS: A document model that encodes structured clinical information in patient reports while retaining the original contents was designed using the extensible markup language (XML), and a document type definition (DTD) was created. An existing natural language processor (NLP) was modified to generate output consistent with the model. Two hundred reports were processed using the modified NLP system, and the XML output that was generated was validated using an XML validating parser. RESULTS: The modified NLP system successfully processed all 200 reports. The output of one report was invalid, and 199 reports were valid XML forms consistent with the DTD. CONCLUSIONS: Natural language processing can be used to automatically create an enriched document that contains a structured component whose elements are linked to portions of the original textual report. This integrated document model provides a representation where documents containing specific information can be accurately and efficiently retrieved by querying the structured components. If manual review of the documents is desired, the salient information in the original reports can also be identified and highlighted. Using an XML model of tagging provides an additional benefit in that software tools that manipulate XML documents are readily available.

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HumansInformation Storage and RetrievalMedical Records Systems, ComputerizedNatural Language ProcessingProgramming LanguagesJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

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C Friedman, G Hripcsak, L Shagina, H Liu. Representing information in patient reports using natural language processing and the extensible markup language.. https://doi.org/10.1136/jamia.1999.0060076

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