title: Assessing surgical site infection risk factors using electronic medical records and text mining.
abstract: Text mining techniques to detect surgical site infections (SSI) in unstructured clinical notes were used to improve SSI detection. In conjuction with data from an integrated electronic medical record, all of the 22 SSIs detected by traditional hospital-based surveillance were found using text mining, along with an additional 37 SSIs not detected by traditional surveillance.
authors.affiliation: Departments of Orthopedics and Rehabilitation, Burlington, VT. Electronic address: james.michelson@uvm.edu.
authors.forename: James D
authors.initials: JD
authors.lastname: Michelson
authors.affiliation: Departments of Orthopedics and Rehabilitation, Burlington, VT.
authors.forename: Jenna S
authors.initials: JS
authors.lastname: Pariseau
authors.affiliation: Departments of Anesthesia, University of Vermont College of Medicine, Burlington, VT.
authors.forename: William C
authors.initials: WC
authors.lastname: Paganelli
mesh_terms: D000328:Adult; D000368:Aged; D015331:Cohort Studies; D057225:Data Mining; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment; D013530:Surgical Wound Infection / Q000453:epidemiology*
keywords: Surgery; Surveillance