title: Optimizing A syndromic surveillance text classifier for influenza-like illness: Does document source matter?
abstract: Syndromic surveillance systems that incorporate electronic free-text data have primarily focused on extracting concepts of interest from chief complaint text, emergency department visit notes, and nurse triage notes. Due to availability and access, there has been limited work in the area of surveilling the full text of all electronic note documents compared with more specific document sources. This study provides an evaluation of the performance of a text classifier for detection of influenza-like illness (ILI) by document sources that are commonly used for biosurveillance by comparing them to routine visit notes, and a full electronic note corpus approach. Evaluating the performance of an automated text classifier for syndromic surveillance by source document will inform decisions regarding electronic textual data sources for potential use by automated biosurveillance systems. Even when a full electronic medical record is available, commonly available surveillance source documents provide acceptable statistical performance for automated ILI surveillance.
authors.affiliation: VA Salt Lake City Health Care System, Department of Internal Medicine, University of Utah School of Medicine, USA.
authors.forename: Brett R
authors.initials: BR
authors.lastname: South
authors.forename: Brett Ray
authors.initials: BR
authors.lastname: South
authors.forename: Wendy W
authors.initials: WW
authors.lastname: Chapman
authors.forename: Wendy
authors.initials: W
authors.lastname: Chapman
authors.forename: Sylvain
authors.initials: S
authors.lastname: Delisle
authors.forename: Shuying
authors.initials: S
authors.lastname: Shen
authors.forename: Ericka
authors.initials: E
authors.lastname: Kalp
authors.forename: Trish
authors.initials: T
authors.lastname: Perl
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Samore
authors.forename: Adi V
authors.initials: AV
authors.lastname: Gundlapalli
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004282:Documentation / Q000379:methods*; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis* / Q000453:epidemiology; D016247:Information Storage and Retrieval / Q000379:methods*; D008487:Medical History Taking / Q000706:statistics & numerical data*; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013577:Syndrome; D014481:United States; D018875:Vocabulary, Controlled