title: Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert.
abstract: BACKGROUND
  We designed and validated a rule-based expert system to identify influenza like illness (ILI) from routinely recorded general practice clinical narrative to aid a larger retrospective research study into the impact of the 2009 influenza pandemic in New Zealand.
  
  
  METHODS
  Rules were assessed using pattern matching heuristics on routine clinical narrative. The system was trained using data from 623 clinical encounters and validated using a clinical expert as a gold standard against a mutually exclusive set of 901 records.
  
  
  RESULTS
  We calculated a 98.2 % specificity and 90.2 % sensitivity across an ILI incidence of 12.4 % measured against clinical expert classification. Peak problem list identification of ILI by clinical coding in any month was 9.2 % of all detected ILI presentations. Our system addressed an unusual problem domain for clinical narrative classification; using notational, unstructured, clinician entered information in a community care setting. It performed well compared with other approaches and domains. It has potential applications in real-time surveillance of disease, and in assisted problem list coding for clinicians.
  
  
  CONCLUSIONS
  Our system identified ILI presentation with sufficient accuracy for use at a population level in the wider research study. The peak coding of 9.2 % illustrated the need for automated coding of unstructured narrative in our study.
authors.affiliation: Patients First, Wellington, New Zealand. jayden.macrae@patientsfirst.org.nz.
authors.forename: Jayden
authors.initials: J
authors.lastname: MacRae
authors.affiliation: Sapere Research Group, Wellington, New Zealand.
authors.forename: Tom
authors.initials: T
authors.lastname: Love
authors.affiliation: Department of Public Health, University of Otago Wellington, Wellington, New Zealand.
authors.forename: Michael G
authors.initials: MG
authors.lastname: Baker
authors.affiliation: Department of Primary Health Care & General Practice, University of Otago Wellington, Wellington, New Zealand.
authors.forename: Anthony
authors.initials: A
authors.lastname: Dowell
authors.affiliation: Compass Health, Wellington, New Zealand.
authors.forename: Matthew
authors.initials: M
authors.lastname: Carnachan
authors.affiliation: Department of Primary Health Care & General Practice, University of Otago Wellington, Wellington, New Zealand.
authors.forename: Maria
authors.initials: M
authors.lastname: Stubbe
authors.affiliation: Department of Primary Health Care & General Practice, University of Otago Wellington, Wellington, New Zealand.
authors.forename: Lynn
authors.initials: L
authors.lastname: McBain
mesh_terms: D005103:Expert Systems; D058006:General Practice; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis*; D033262:Narration; D009323:Natural Language Processing