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


CONCLUSIONS
We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.
Zeng
Qing T
QT
Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA. qzeng@dsg.harvard.edu
Goryachev
Sergey
S
Weiss
Scott
S
Sordo
Margarita
M
Murphy
Shawn N
SN
Lazarus
Ross
R
D001249:Asthma / Q000150:complications / Q000175:diagnosis*; D015897:Comorbidity; D006801:Humans; D038801:International Classification of Diseases; D016347:Medical Records Systems, Computerized / Q000592:standards*; D009323:Natural Language Processing; D010351:Patient Discharge; D029424:Pulmonary Disease, Chronic Obstructive / Q000150:complications / Q000175:diagnosis; D012680:Sensitivity and Specificity; D012907:Smoking / Q000453:epidemiology