ArticleJournal of the American Medical Informatics Association : JAMIA2015
Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing.Methods of information in medicine · 2020Article
- Words prediction based on N-gram model for free-text entry in electronic health records.Health information science and systems · 2019Article
- A computational framework for converting textual clinical diagnostic criteria into the quality data model.Journal of biomedical informatics · 2016Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveElectronic medical records with encoded entries should enhance the semantic interoperability of document exchange. However, it remains a challenge to encode the narrative concept and to transform the coded concepts into a standard entry-level document. This study aimed to use a novel approach for the generation of entry-level interoperable clinical documents.
methodsUsing HL7 clinical document architecture (CDA) as the example, we developed three pipelines to generate entry-level CDA documents. The first approach was a semi-automatic annotation pipeline (SAAP), the second was a natural language processing (NLP) pipeline, and the third merged the above two pipelines. We randomly selected 50 test documents from the i2b2 corpora to evaluate the performance of the three pipelines.
resultsThe 50 randomly selected test documents contained 9365 words, including 588 Observation terms and 123 Procedure terms. For the Observation terms, the merged pipeline had a significantly higher F-measure than the NLP pipeline (0.89 vs 0.80, p<0.0001), but a similar F-measure to that of the SAAP (0.89 vs 0.87). For the Procedure terms, the F-measure was not significantly different among the three pipelines.
conclusionsThe combination of a semi-automatic annotation approach and the NLP application seems to be a solution for generating entry-level interoperable clinical documents.
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