Identification of severe acute pancreatitis using an artificial neural network

Surgery

Volumn 141, Issue 1, 2007, Pages 59-66

  Mofidi, Reza ^a   Duff, Michael D ^a   Madhavan, Krishna K ^a   Garden, Oliver J ^a   Parks, Rowan W ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AMYLASE; GLUCOSE; LACTATE DEHYDROGENASE; UREA;

ACUTE PANCREATITIS; ADULT; AGED; AMYLASE BLOOD LEVEL; APACHE; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER PROGRAM; DISEASE COURSE; DISEASE SEVERITY; FEMALE; GLASGOW COMA SCALE; GLUCOSE BLOOD LEVEL; HUMAN; LACTATE DEHYDROGENASE BLOOD LEVEL; LAPAROTOMY; MAJOR CLINICAL STUDY; MALE; MEDICAL DECISION MAKING; OUTCOMES RESEARCH; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; SCORING SYSTEM; UREA BLOOD LEVEL;

ADULT; AGED; AGED, 80 AND OVER; APACHE; FEMALE; GLASGOW OUTCOME SCALE; HUMANS; MALE; MIDDLE AGED; MULTIPLE ORGAN FAILURE; NEURAL NETWORKS (COMPUTER); PANCREATITIS; PROGNOSIS; SCOTLAND;

EID: 33845502065     PISSN: 00396060     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.surg.2006.07.022     Document Type: Article

References (37)

1. Triester S.L., and Kowdley K.V. Prognostic factors in acute pancreatitis. J Clin Gastroenterol 34 (2002) 167-176
2. Abu-Zidan F.M., Bonham M.J., and Windsor J.A. Severity of acute pancreatitis: a multivariate analysis of oxidative stress markers and modified Glasgow criteria. Br J Surg 87 (2000) 1019-1023
3. Mitchell R.M., Byrne M.F., and Baillie J. Pancreatitis. Lancet 361 (2003) 1447-1455
4. Folsch U.R., Nitsche R., Ludtke R., Hilgers R.A., and Creutzfeldt W. Early ERCP and papillotomy compared with conservative treatment for acute biliary pancreatitis. The German Study Group on Acute Biliary Pancreatitis. N Engl J Med 336 (1997) 237-242
5. Mier J., Leon E.L., Castillo A., Robledo F., and Blanco R. Early versus late necrosectomy in severe necrotizing pancreatitis. Am J Surg 173 (1997) 71-75
6. Ranson J.H., Rifkind K.M., Roses D.F., Fink S.D., Eng K., and Spencer F.C. Prognostic signs and the role of operative management in acute pancreatitis. Surg Gynecol Obstet 139 (1974) 69-81
7. Wilson C., Heath D.I., and Imrie C.W. Prediction of outcome in acute pancreatitis: a comparative study of APACHE II, clinical assessment and multiple factor scoring systems. Br J Surg 77 (1990) 1260-1264
8. Eachempati S.R., Hydo L.J., and Barie P.S. Severity scoring for prognostication in patients with severe acute pancreatitis. Comparative analysis of the Ranson score and the APACHE III score. Arch Surg 137 (2002) 730-736
9. Cross S.S., Harrison R., and Kennedy R.L. Introduction to neural networks. Lancet 346 (1995) 1057-1063
10. Warner B. Understanding neural networks as statistical tools. American Statistician 50 (1996) 284-294
11. Drew P.J., and Monson J.R. Artificial neural networks. Surgery 127 (2000) 3-11
12. Baxt W.G. Application of artificial neural networks to clinical medicine. Lancet 346 (1995) 1135-1138
13. Gregory J.S., Junold R.M., Undrill P.E., and Aspden R.M. Analysis of trabecular bone structure using Fourier transforms and neural networks. IEEE Trans Inf Technol Biomed 3 (1999) 289-294
14. Innocent P.R., Barnes M., and John R. Application of the fuzzy ART/MAP and MINMAX/MAP neural network models to radiographic image classification. Artif Intell Med 11 (1997) 241-263
15. Dawson A.E., Austin R.E., and Weinberg D.S. Nuclear grading of breast carcinoma by image analysis. Classification by multivariate and neural network analysis. Am J Clin Pathol 95 (1991) S29-S37
16. Finne P., Finne R., Auvinen A., Juusela H., Aro J., Maattanen L., et al. Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. Urology 56 (2000) 418-422
17. Bottaci L., Drew P.J., Hartley J.E., Hadfield M.B., Farouk R., Lee P.W., et al. Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet 350 (1997) 469-472
18. Dybowski R., Weller P., Chang R., and Gant V. Prediction of outcome in critically ill patients using artificial neural network synthesized by genetic algorithm. Lancet 347 (1996) 1146-1150
19. Aitken R.J., Nixon S.J., and Ruckley C.V. Lothian surgical audit: a 15-year experience of improvement in surgical practice through regional computerised audit. Lancet 350 (1997) 800-804
20. Bone R.C. Immunologic dissonance: a continuing evolution in our understanding of the systemic inflammatory response syndrome and multiple organ dysfunction syndrome (MODS). Ann of Intern Med 125 (1996) 680-687
21. Bradley III E.L. A clinically based classification system for acute pancreatitis. Summary of the International Symposium on Acute Pancreatitis, Atlanta, Ga, September 11 through 13, 1992. Arch Surg 128 (1993) 586-590
22. Marshall J.C., Cook D.J., Christou N.V., Bernard G.R., Sprung C.L., and Sibbald W.J. Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. Crit Care Med 23 (1995) 1638-1652
23. Hunter A., Kennedy L., Henry J., and Ferguson I. Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Comput Methods Programs Biomed 62 (2000) 11-19
24. Hanley J.A., and McNeil B.J. The meaning and use of area under receiver operator characteristic (ROC) curve. Radiology 143 (1982) 29-36
25. Simel D.L., Samsa G.P., and Matchar D.B. Likelihood ratios with confidence: sample size estimation for diagnostic test studies. J of Clin Epidemiol 44 (1991) 763-770
26. Blakeley D.D., Oddone E.Z., Hasselblad V., Simel D.L., and Matchar D.B. Non-invasive carotid artery testing-meta-analytic review. Ann Intern Med 12 (1995) 360-367
27. Corfield A.P., Cooper M.J., Williamson R.C., Mayer A.D., McMahon M.J., Dickson A.P., et al. Prediction of severity in acute pancreatitis: prospective comparison of three prognostic indices. Lancet 2 (1985) 403-407
28. McMahon M.J., Playforth M.J., and Pickford I.R. A compaative study of methods for the prediction of severity of attacks of acute pancreatitis. Br J Surg 67 (1980) 22-25
29. Blamey S.L., Imrie C.W., O'Neill J., Gilmour W.H., and Carter D.C. Prognostic factors in acute pancreatitis. Gut 25 (1984) 1340-1346
30. Balthazar E.J., Robinson D.L., Megibow A.J., and Ranson J.H. Acute pancreatitis: value of CT in establishing prognosis. Radiology 174 (1990) 331-336
31. Balthazar E.J. Staging of acute pancreatitis. Radiol Clin North Am 40 (2002) 1199-1209
32. Mentula P., Kylanpaa M.L., Kemppainen E., Jansson S.E., Sarna S., Puolakkainen P., et al. Early prediction of organ failure by combined markers in patients with acute pancreatitis. Br J Surg 92 (2005) 68-75
33. Buter A., Imrie C.W., Carter C.R., Evans S., and McKay C.J. Dynamic nature of early organ dysfunction determines outcome in acute pancreatitis. Br J Surg 89 (2002) 298-302
34. Burke H.B., Goodman P.H., Rosen D.B., Henson D.E., Weinstein J.N., Harrell Jr. F.E., et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 79 (1997) 857-862
35. Pofahl W.E., Walczak S.M., Rhone E., and Izenberg S.D. Use of an artificial neural network to predict length of stay in acute pancreatitis. Am Surg 64 (1998) 868-872
36. Keogan M.T., Lo J.Y., Freed K.S., Raptopoulos V., Blake S., Kamel I.R., et al. Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Acad Radiol 9 (2002) 410-419
37. Halonen K.I., Leppaniemi A.K., Lundin J.E., Puolakkainen P.A., Kemppainen E.A., and Haapiainen R.K. Predicting fatal outcome in the early phase of severe acute pancreatitis by using novel prognostic models. Pancreatology 3 (2003) 309-315