Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system - PubMed (original) (raw)

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

Qing T Zeng et al. BMC Med Inform Decis Mak. 2006.

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.

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Figure 1

Processing Flow Diagram.

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