title: Detection of infectious symptoms from VA emergency department and primary care clinical documentation.
abstract: OBJECTIVE
  The majority of clinical symptoms are stored as free text in the clinical record, and this information can inform clinical decision support and automated surveillance efforts if it can be accurately processed into computer interpretable data.
  
  
  METHODS
  We developed rule-based algorithms and evaluated a natural language processing (NLP) system for infectious symptom detection using clinical narratives. Training (60) and testing (444) documents were randomly selected from VA emergency department, urgent care, and primary care records. Each document was processed with NLP and independently manually reviewed by two clinicians with adjudication by referee. Infectious symptom detection rules were developed in the training set using keywords and SNOMED-CT concepts, and subsequently evaluated using the testing set.
  
  
  RESULTS
  Overall symptom detection performance was measured with a precision of 0.91, a recall of 0.84, and an F measure of 0.87. Overall symptom detection with assertion performance was measured with a precision of 0.67, a recall of 0.62, and an F measure of 0.64. Among those instances in which the automated system matched the reference set determination for symptom, the system correctly detected 84.7% of positive assertions, 75.1% of negative assertions, and 0.7% of uncertain assertions.
  
  
  CONCLUSIONS
  This work demonstrates how processed text could enable detection of non-specific symptom clusters for use in automated surveillance activities.
authors.affiliation: Geriatrics Research, Education, and Clinical Care, Tennessee Valley Healthcare System, Veteran's Administration, Nashville, TN 37212, USA. michael@matheny.info
authors.forename: Michael E
authors.initials: ME
authors.lastname: Matheny
authors.forename: Fern
authors.initials: F
authors.lastname: Fitzhenry
authors.forename: Theodore
authors.initials: T
authors.lastname: Speroff
authors.forename: Jennifer K
authors.initials: JK
authors.lastname: Green
authors.forename: Michelle L
authors.initials: ML
authors.lastname: Griffith
authors.forename: Eduard E
authors.initials: EE
authors.lastname: Vasilevskis
authors.forename: Elliot M
authors.initials: EM
authors.lastname: Fielstein
authors.forename: Peter L
authors.initials: PL
authors.lastname: Elkin
authors.forename: Steven H
authors.initials: SH
authors.lastname: Brown
mesh_terms: D000465:Algorithms; D003141:Communicable Diseases / Q000175:diagnosis*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted; D004636:Emergency Service, Hospital; D006787:Hospitals, Veterans; D006801:Humans; D007239:Infections / Q000175:diagnosis*; D016347:Medical Records Systems, Computerized / Q000458:organization & administration*; D011159:Population Surveillance; D011320:Primary Health Care