title: Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.
abstract: Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks.
  
  
  
  Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10-20, and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability.
authors.affiliation: Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Fei
authors.affiliation: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China.
authors.forename: J
authors.initials: J
authors.lastname: Hu
authors.affiliation: Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
authors.forename: W-Q
authors.initials: WQ
authors.lastname: Li
authors.affiliation: Department of General Surgery, Bayi Hospital affiliated Nanjing University of Chinese Medicine/the 81st Hospital of P.L.A., Nanjing, China.
authors.forename: W
authors.initials: W
authors.lastname: Wang
authors.affiliation: Department of General Surgery, Bayi Hospital affiliated Nanjing University of Chinese Medicine/the 81st Hospital of P.L.A., Nanjing, China.
authors.forename: G-Q
authors.initials: GQ
authors.lastname: Zong
mesh_terms: D000208:Acute Disease; D000328:Adult; D000368:Aged; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010195:Pancreatitis / Q000150:complications* / Q000453:epidemiology; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012044:Regression Analysis; D012189:Retrospective Studies; D012984:Software; D018608:Ultrasonography, Doppler; D020246:Venous Thrombosis / Q000150:complications / Q000175:diagnosis* / Q000453:epidemiology*; D055815:Young Adult
keywords: forecasting; logistic regression; neural network models; pancreatitis; portosplenomesenteric venous thrombosis