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.
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.
Authors: Balázs Kui; József Pintér; Roland Molontay; Marcell Nagy; Nelli Farkas; Noémi Gede; Áron Vincze; Judit Bajor; Szilárd Gódi; József Czimmer; Imre Szabó; Anita Illés; Patrícia Sarlós; Roland Hágendorn; Gabriella Pár; Mária Papp; Zsuzsanna Vitális; György Kovács; Eszter Fehér; Ildikó Földi; Ferenc Izbéki; László Gajdán; Roland Fejes; Balázs Csaba Németh; Imola Török; Hunor Farkas; Artautas Mickevicius; Ville Sallinen; Shamil Galeev; Elena Ramírez-Maldonado; Andrea Párniczky; Bálint Erőss; Péter Jenő Hegyi; Katalin Márta; Szilárd Váncsa; Robert Sutton; Peter Szatmary; Diane Latawiec; Chris Halloran; Enrique de-Madaria; Elizabeth Pando; Piero Alberti; Maria José Gómez-Jurado; Alina Tantau; Andrea Szentesi; Péter Hegyi Journal: Clin Transl Med Date: 2022-06
Authors: Matthew J Dimagno; Erik-Jan Wamsteker; Rafat S Rizk; Joshua P Spaete; Suraj Gupta; Tanya Sahay; Jeffrey Costanzo; John M Inadomi; Lena M Napolitano; Robert C Hyzy; Jeff S Desmond Journal: Am J Gastroenterol Date: 2014-03 Impact factor: 10.864
Authors: Péter Hegyi; Andrea Szentesi; Szabolcs Kiss; József Pintér; Roland Molontay; Marcell Nagy; Nelli Farkas; Zoltán Sipos; Péter Fehérvári; László Pecze; Mária Földi; Áron Vincze; Tamás Takács; László Czakó; Ferenc Izbéki; Adrienn Halász; Eszter Boros; József Hamvas; Márta Varga; Artautas Mickevicius; Nándor Faluhelyi; Orsolya Farkas; Szilárd Váncsa; Rita Nagy; Stefania Bunduc; Péter Jenő Hegyi; Katalin Márta; Katalin Borka; Attila Doros; Nóra Hosszúfalusi; László Zubek; Bálint Erőss; Zsolt Molnár; Andrea Párniczky Journal: Sci Rep Date: 2022-05-12 Impact factor: 4.996