title: Predicting active pulmonary tuberculosis using an artificial neural network.
abstract: BACKGROUND
  Nosocomial outbreaks of tuberculosis (TB) have been attributed to unrecognized pulmonary TB. Accurate assessment in identifying index cases of active TB is essential in preventing transmission of the disease.
  
  
  OBJECTIVE
  To develop an artificial neural network using clinical and radiographic information to predict active pulmonary TB at the time of presentation at a health-care facility that is superior to physicians' opinion.
  
  
  METHODS
  Nonconcurrent prospective study.
  
  
  METHODS
  University-affiliated hospital.
  
  
  METHODS
  A derivation group of 563 isolation episodes and a validation group of 119 isolation episodes.
  
  
  METHODS
  A general regression neural network (GRNN) was used to develop the predictive model.
  
  
  METHODS
  Predictive accuracy of the neural network compared with clinicians' assessment.
  
  
  RESULTS
  Predictive accuracy was assessed by the c-index, which is equivalent to the area under the receiver operating characteristic curve. The GRNN significantly outperformed the physicians' prediction, with calculated c-indices (+/- SEM) of 0.947 +/- 0.028 and 0.61 +/- 0.045, respectively (p < 0.001). When the GRNN was applied to the validation group, the corresponding c-indices were 0. 923 +/- 0.056 and 0.716 +/- 0.095, respectively.
  
  
  CONCLUSIONS
  An artificial neural network can identify patients with active pulmonary TB more accurately than physicians' clinical assessment.
authors.affiliation: Department of Medicine, Erie County Medical Center, Buffalo, NY 14215, USA. solh@buffalo.edu
authors.forename: A A
authors.initials: AA
authors.lastname: El-Solh
authors.forename: C B
authors.initials: CB
authors.lastname: Hsiao
authors.forename: S
authors.initials: S
authors.lastname: Goodnough
authors.forename: J
authors.initials: J
authors.lastname: Serghani
authors.forename: B J
authors.initials: BJ
authors.lastname: Grant
mesh_terms: D017088:AIDS-Related Opportunistic Infections / Q000175:diagnosis / Q000453:epidemiology; D003428:Cross Infection / Q000175:diagnosis / Q000453:epidemiology; D003936:Diagnosis, Computer-Assisted; D004196:Disease Outbreaks; D006785:Hospitals, University; D006801:Humans; D016571:Neural Networks, Computer; D009518:New York; D010343:Patient Admission / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012044:Regression Analysis; D015203:Reproducibility of Results; D014397:Tuberculosis, Pulmonary / Q000175:diagnosis* / Q000453:epidemiology