Extracting Principal Diagnosis, Co-morbidity and Smoking Status for Asthma Research: Evaluation of a Natural Language Processing System

Overview

Journal BMC Med Inform Decis Mak

Publisher Biomed Central

Specialty Medical Informatics

Date 2006 Jul 29

PMID 16872495

Citations 186

Authors

Qing T Zeng

Sergey Goryachev

Scott Weiss

Margarita Sordo

Shawn N Murphy

Ross Lazarus

Affiliations

Soon will be listed here.

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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