Over the last few decades researchers have actively applied NLP techniques to the medical domain (Friedman and Hripcsak, 1999, Spyns, 1996). NLP techniques have been used for a variety of applications, including quality assessment in radiology (Fiszman et al., 1998, Chapman et al., 2001b); identification of structures in radiology images (Sinha et al., 2001a, Sinha et al., 2001b); facilitation of structured reporting (Morioka et al., 2002, Sinha et al., 2000) and order entry (Wilcox et al., 2002, Lovis et al., 2001); encoding variables required by automated decision-support systems such as guidelines (Fiszman and Haug, 2000), diagnostic systems (Aronsky et al., 2001), and antibiotic therapy alarms (Fiszman et al., 2000); detecting patients with suspected tuberculosis (Jain et al., 1996, Knirsch et al., 1998, Hripcsak et al., 1999); identifying findings suspicious for breast cancer (Jain and Friedman, 1997), stroke (Elkins et al., 2000), and community acquired pneumonia (Friedman et al., 1999b); and deriving comorbidities from text (Chuang et al., 2002).
Probably the most widely used and evaluated NLP system in the medical domain is MedLEE, which was created at Columbia Presbyterian Medical Center (Friedman, 2000, Friedman et al., 1994,1998,1999a). MedLEE extracts clinical information from several types of radiology reports, discharge summaries, visit notes, electrocardiography, echocardiography, and pathology notes. MedLEE has been shown to be as accurate as physicians at extracting clinical concepts from chest radiograph reports (Hripcsak et al., 1995,2002).
NLP has only recently been applied to the domain of outbreak and disease surveillance, and most of the research has focused on processing free-text chief complaints recorded in the ED (Olszewski, 2003, Ivanov et al., 2002, Ivanov et al., 2003, Travers et al., 2003, Travers and Haas, 2003, Chapman et al., 2005a).
Below we describe some of the statistical and symbolic NLP techniques implemented in the medical domain.
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