title: Outcome analysis of patients with acute pancreatitis by using an artificial neural network.
abstract: OBJECTIVE
  The authors performed this study to evaluate the ability of an artificial neural network (ANN) that uses radiologic and laboratory data to predict the outcome in patients with acute pancreatitis.
  
  
  METHODS
  An ANN was constructed with data from 92 patients with acute pancreatitis who underwent computed tomography (CT). Input nodes included clinical, laboratory, and CT data. The ANN was trained and tested by using a round-robin technique, and the performance of the ANN was compared with that of linear discriminant analysis and Ranson and Balthazar grading systems by using receiver operating characteristic analysis. The length of hospital stay was used as an outcome measure.
  
  
  RESULTS
  Hospital stay ranged from 0 to 45 days, with a mean of 8.4 days. The hospital stay was shorter than the mean for 62 patients and longer than the mean for 30. The 23 input features were reduced by using stepwise linear discriminant analysis, and an ANN was developed with the six most statistically significant parameters (blood pressure, extent of inflammation, fluid aspiration, serum creatinine level, serum calcium level, and the presence of concurrent severe illness). With these features, the ANN successfully predicted whether the patient would exceed the mean length of stay (Az = 0.83 +/- 0.05). Although the Az performance of the ANN was statistically significantly better than that of the Ranson (Az = 0.68 +/- 0.06, P < .02) and Balthazar (Az = 0.62 +/- 0.06, P < .003) grades, it was not significantly better than that of linear discriminant analysis (Az = 0.82 +/- 0.05, P = .53).
  
  
  CONCLUSIONS
  An ANN may be useful for predicting outcome in patients with acute pancreatitis.
authors.affiliation: Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02216, USA.
authors.forename: Mary T
authors.initials: MT
authors.lastname: Keogan
authors.forename: Joseph Y
authors.initials: JY
authors.lastname: Lo
authors.forename: Kelly S
authors.initials: KS
authors.lastname: Freed
authors.forename: Vasillios
authors.initials: V
authors.lastname: Raptopoulos
authors.forename: Simon
authors.initials: S
authors.lastname: Blake
authors.forename: Ihab R
authors.initials: IR
authors.lastname: Kamel
authors.forename: K
authors.initials: K
authors.lastname: Weisinger
authors.forename: Max P
authors.initials: MP
authors.lastname: Rosen
authors.forename: Rendon C
authors.initials: RC
authors.lastname: Nelson
mesh_terms: D000208:Acute Disease; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007902:Length of Stay; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care; D010195:Pancreatitis / Q000453:epidemiology*; D011379:Prognosis; D012372:ROC Curve