Research article

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

Qing T Zeng¹ , Sergey Goryachev¹ , Scott Weiss² , Margarita Sordo² , Shawn N Murphy³ and Ross Lazarus²

¹Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA

²Channing Laboratory, Brigham and Women's Hospital, Boston, MA, USA

³Laboratory of Computer Science, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

author email corresponding author email

BMC Medical Informatics and Decision Making 2006, 6:30doi:10.1186/1472-6947-6-30

Published:	26 July 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.