Premium
Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis
Author(s) -
Fei Y.,
Hu J.,
Li W.Q.,
Wang W.,
Zong G.Q.
Publication year - 2017
Publication title -
journal of thrombosis and haemostasis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.947
H-Index - 178
eISSN - 1538-7836
pISSN - 1538-7933
DOI - 10.1111/jth.13588
Subject(s) - logistic regression , artificial neural network , medicine , acute pancreatitis , machine learning , venous thrombosis , predictive modelling , deep vein , artificial intelligence , thrombosis , computer science
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.Summary Objective To construct and validate artificial neural networks ( ANN s) for predicting the occurrence of portosplenomesenteric venous thrombosis ( PSMVT ) and compare the predictive ability of the ANN s with that of logistic regression. Methods The ANN s and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis ( AP ) patients. The ANN s 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 SPSS 17.0 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 ANN s 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 ANN s 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 ANN s 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 ANN s 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 ANN s modeling to improve its predictive ability.