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Outcome analysis of patients with acute pancreatitis by using an artificial neural network.

Publication ,  Journal Article
Keogan, MT; Lo, JY; Freed, KS; Raptopoulos, V; Blake, S; Kamel, IR; Weisinger, K; Rosen, MP; Nelson, RC
Published in: Acad Radiol
April 2002

RATIONALE AND OBJECTIVES: 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. MATERIALS AND 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). CONCLUSION: An ANN may be useful for predicting outcome in patients with acute pancreatitis.

Duke Scholars

Published In

Acad Radiol

DOI

ISSN

1076-6332

Publication Date

April 2002

Volume

9

Issue

4

Start / End Page

410 / 419

Location

United States

Related Subject Headings

  • ROC Curve
  • Prognosis
  • Pancreatitis
  • Outcome Assessment, Health Care
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Middle Aged
  • Male
  • Length of Stay
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Keogan, M. T., Lo, J. Y., Freed, K. S., Raptopoulos, V., Blake, S., Kamel, I. R., … Nelson, R. C. (2002). Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Acad Radiol, 9(4), 410–419. https://doi.org/10.1016/s1076-6332(03)80186-1
Keogan, Mary T., Joseph Y. Lo, Kelly S. Freed, Vasillios Raptopoulos, Simon Blake, Ihab R. Kamel, K. Weisinger, Max P. Rosen, and Rendon C. Nelson. “Outcome analysis of patients with acute pancreatitis by using an artificial neural network.Acad Radiol 9, no. 4 (April 2002): 410–19. https://doi.org/10.1016/s1076-6332(03)80186-1.
Keogan MT, Lo JY, Freed KS, Raptopoulos V, Blake S, Kamel IR, et al. Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Acad Radiol. 2002 Apr;9(4):410–9.
Keogan, Mary T., et al. “Outcome analysis of patients with acute pancreatitis by using an artificial neural network.Acad Radiol, vol. 9, no. 4, Apr. 2002, pp. 410–19. Pubmed, doi:10.1016/s1076-6332(03)80186-1.
Keogan MT, Lo JY, Freed KS, Raptopoulos V, Blake S, Kamel IR, Weisinger K, Rosen MP, Nelson RC. Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Acad Radiol. 2002 Apr;9(4):410–419.
Journal cover image

Published In

Acad Radiol

DOI

ISSN

1076-6332

Publication Date

April 2002

Volume

9

Issue

4

Start / End Page

410 / 419

Location

United States

Related Subject Headings

  • ROC Curve
  • Prognosis
  • Pancreatitis
  • Outcome Assessment, Health Care
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Middle Aged
  • Male
  • Length of Stay
  • Humans