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 smokin...

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Detalles Bibliográficos
Autores principales: Zeng, Qing T, Goryachev, Sergey, Weiss, Scott, Sordo, Margarita, Murphy, Shawn N, Lazarus, Ross
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1553439/
https://www.ncbi.nlm.nih.gov/pubmed/16872495
http://dx.doi.org/10.1186/1472-6947-6-30
Descripción
Sumario: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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