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

• Main Authors: Sordo Margarita, Weiss Scott, Goryachev Sergey, Zeng Qing T, Murphy Shawn N, Lazarus Ross
• Format: Article
• Language: English
• Published: BMC 2006-07-01
• Series: BMC Medical Informatics and Decision Making
• Online Access: http://www.biomedcentral.com/1472-6947/6/30

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.</p>