Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2006-12

AUTHORS

Qing T Zeng, Sergey Goryachev, Scott Weiss, Margarita Sordo, Shawn N Murphy, Ross Lazarus

ABSTRACT

BACKGROUND: The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease. METHODS: The principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard. RESULTS: The accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded. CONCLUSION: We consider the results promising, given the complexity of the discharge summaries and the extraction tasks. More... »

PAGES

30

References to SciGraph publications

  • 2005. On Sample Size and Classification Accuracy: A Performance Comparison in BIOLOGICAL AND MEDICAL DATA ANALYSIS
  • 2005-12. Automation of a problem list using natural language processing in BMC MEDICAL INFORMATICS AND DECISION MAKING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1472-6947-6-30

    DOI

    http://dx.doi.org/10.1186/1472-6947-6-30

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1024163546

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/16872495


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