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Combining open-source natural language processing tools to parse clinical practice guidelines
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DOI：10.1111/j.1468-0394.2010.00575.x.png)
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Abstract
En 中文
Natural language processing (NLP) has been used to process text pertaining to patient records and narratives. However, most of the methods used were developed for specific systems, so new research is necessary to assess whether such methods can be easily retargeted for new applications and goals, with the same performance. In this paper, open-source tools are reused as building blocks on which a new system is built. The aim of our work is to evaluate the applicability of the current NLP technology to a new domain: automatic knowledge acquisition of diagnostic and therapeutic procedures from clinical practice guideline free-text documents. In order to do this, two publicly available syntactic parsers, several terminology resources and a tool oriented to identify semantic predications were tailored to increase the performance of each tool individually. We apply this new approach to 171 sentences selected by the experts from a clinical guideline, and compare the results with those of the tools applied with no tailoring. The results of this paper show that with some adaptation, open-source NLP tools can be retargeted for new tasks, providing an accuracy that is equivalent to the methods designed for specific tasks.
Keywords:
knowledge acquisition
natural language processing
knowledge representation
clinical practice guidelines
UMLS
SemRep
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