Artificial neural network algorithm model as powerful tool to predict acute lung injury following to severe acute pancreatitis

Pancreatology xxx (2018) 1e8

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Artificial neural network algorithm model as powerful tool to predict acute lung injury following to severe acute pancreatitis Yang Fei, Kun Gao, Wei-qin Li* Medical School of Nanjing University, Jinling Hospital/Nanjing General Hospital of Nanjing Military Region of P.L.A., P.L.A. Research Institute of General Surgery, Nanjing, 210002, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 June 2018 Received in revised form 21 September 2018 Accepted 24 September 2018 Available online xxx

Objective: The aim of this study is to predict the risk of severe acute pancreatitis (SAP) associated with acute lung injury (ALI) by artificial neural networks (ANNs) model. Methods: The ANNs and logistic regression model were constructed using clinical and laboratory data of 217 SAP patients. The models were first trained on 152 randomly chosen patients, validated and tested on the 33 patients and 32 patients respectively. Statistical indices were used to evaluate the value of the forecast in two models. Results: The training set, validation set and test set were not significantly different for any of the 13 variables. After training, the back propagation network retained excellent pattern recognition ability. When the ANNs model was applied to the test set, it revealed a sensitivity of 87.5%, specificity of 83.3%. The accuracy was 84.43%. Significant differences could be found between ANNs model and logistic regression model in these parameter. When ANNs model was used to identify ALI, the area under receiver operating characteristic curve was 0.859 ± 0.048, which demonstrated the better overall properties than logistic regression modeling (AUC ¼ 0.701 þ 0.041) (95% CI: 0.664e0.857). Meanwhile, pancreatic necrosis rate, lactic dehydrogenase and oxyhemoglobin saturation were the important factors among all thirteen independent variable for ALI. Conclusion: The ANNs model was a valuable tool in dealing with the clinical risk prediction problem of ALI following to SAP. In addition, our approach can extract informative risk factors of ALI via the ANNs model. © 2018 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Keywords: Pancreatitis Lung injury Neural network Logistic regression

Introduction Severe acute pancreatitis (SAP) occurs in 20e25% of all patients with acute pancreatitis. SAP can start with persistent organ dysfunction, ie. pulmonary, renal, or cardiovascular. Furthermore, it can also lead to many complications. SAP commonly leads to SIRS and distant organ complications. The lung is often firstly damaged during the course of the early SIRS/MODS from severe acute pancreatitis confirmed by some studies, and lung injury (ALI) is one of its common complications. ALI is the most common organ failure and approximately 30% of patients with ALI develop its more severe form (lung injury with severe hypoxemia), acute respiratory distress syndrome (ARDS) [1,2]. Onethird of patients presenting with SAP develop including two

groups: ALI with less hypoxemia defined as acute lung injury (ALI) and lung injury with severe hypoxemia defined as acute respiratory distress syndrome (ARDS). These complication is responsible for approximately 60% of all deaths within the first week, furthermore ALI/ARDS in SAP correlate to severe episodes in the initial stage and consequently complicate the clinical course of the disease [3e5]. Preventing the development of ALI and early intervention may be more effective in improving outcomes. Applying a prediction model to identify high-risk patients may alert physicians to avoid specific “second-hit” hospital exposures, such as high tidal volume mechanical ventilation. A more accurate, early predictive tool is needed to facilitate the identification of patients who can benefit from interventions to prevent ALI progression.

* Corresponding author. E-mail address: [email protected] (W.-q. Li). https://doi.org/10.1016/j.pan.2018.09.007 1424-3903/© 2018 IAP and EPC. Published by Elsevier B.V. All rights reserved.

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The diagnosis of ALI following to SAP is mainly based on clinical symptoms, monitoring oxygen saturation and blood gas analysis. However the methods for predicting ALI incidence that include conventional statistical methods (e.g. logistic regression), Cox proportional hazards regression as well as biological markers (Substance P, IL-18, pancreatitis-associated proteins) remain disputable [6,7]. Artificial neural networks (ANNs) are based on the functioning of biological neural networks, which can be used as a nonlinear statistical data modeling tool with the complex relationships between input and output (observed data). ANNs are often referred to as biologically inspired, highly complex analytical technologies, capable of modeling extremely complicated nonlinear functions [8]. ANNs model can analyze the interactions among health risk factors more clearly than statistical methods for its nonlinear fashion. It can be performed to replace these statistical methods to forecast specific medical outcomes by learning from a cohort of similarly treated patients [9,10]. ANNs have been used in clinical medicine for diagnosis; prognosis of survival analyses; clinical outcome in the medical domains of cancer, infectious diseases and critical care [11e13].

Methods General information The patients with SAP were enrolled from the Surgical Intensive Care Unit (SICU) of our hospital from January 2015 to April 2018. Diagnostic criteria of SAP were based on the revision of the Atlanta Classification Consensus [14]. The exclusion criteria were: 1) a history of AP attacks; 2) age＜20 years; 3) SAP with cancer; 4) SAP with heart failure or renal failure; 5) SAP with hematological disease; 6) SAP with chronic lung disease; 6) SAP with active tuberculosis. All of the SAP patients received standard medical treatment according to international guidelines. Ultimately, 217 patients (122 men and 95 women) were

included in the present study. The study protocol conformed to the guidelines formulated by our institutional ethical committee. All patients gave their informed consent to be included in the study. The study was carried out according to the principles of The Declaration of Helsinki. According to the standard American-European Consensus Conference (AECC), ALI is defined as the development of acute, bilateral pulmonary infiltrates and hypoxemia in absence of clinical signs of left atrial hypertension as the main explanation for pulmonary edema [15]. The ratio of arterial oxygen partial pressure to fraction of inspiration oxygen is less than 300 (PaO2/FiO2<300). Artificial neural networks modeling Back propagation (BP) ANN models are composed of three layers of nodes arranged in series: an input layer, a hidden layer and an output layer. Input layer essentially collect the non-linear neurons (i.e., perceptrons) and organize them into a feed forward multilayer structure; every layer is composed of a series of nodes which simulate anthropic neurons (Fig. 1). A total of thirteen input variables including age, gender, body mass index (BMI), white cell count (WBC), fasting blood glucose (FBG), concentration of serum calcium ([Ca2þ]), serum amylase (AMY, somogyi method), temperature (Temp), lactic dehydrogenase (LDH), serum albumin (Alb), c reactive protein (CRP), oxyhemoglobin saturation (SaO2%) and pancreatic necrosis rate (PNR) are applied in current models. The input variables including their description (mean and standard deviation, or frequencies) can be found in Table 1. When the input of input layer is set to xð1Þ; xð2Þ; /; xðR1Þ, the output of input layer neurons corresponding to input of hidden layer neurons can be P displayed as sðiÞ ¼ R1 j¼1 Wði;jÞ*xðjÞ  b1ðiÞ; yðiÞ ¼ f ðsðiÞÞ;i ¼ 1; 2;/; R2. In the formula, Wði; jÞ is the connection weight between input layer neurons (i) and hidden layer neurons (j); b1ðjÞ is the threshold of hidden layer neurons. The full test objects consist of 67 patients with ALI, and 150 patients without ALI. In the hidden layers,

Fig. 1. Diagram showing the structure of the artificial neural networks models.

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Table 1 Input variables and their descriptive statistics. No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Variable code

Age (year) Gender BMI WBC （  109/L) FBG (mmol/L) [Ca2þ] (mmol/L) AMY (Unit) Temp （ C） LDH （IU/L） Alb （g/L） CRP （㎎/L ） SaO2% PNR Output ALI

Variable description

age Gender (man, n%) body mass index white cell count fasting blood glucose concentration of serum calcium serum amylase, somogyi method temperature lactic dehydrogenase serum albumin c reactive protein oxyhemoglobin saturation pancreatic necrosis rate acute lung injury rate

Medians (interquartile ranges) (except No.2,No. 13 and No.14) Training

Validation

Test

49.3 (29.6e61.4) 53.9% 24.6 (19.4e27.6) 13.05 (8.60e17.92) 7.31 (5.82e9.11) 2.14 (2.01e2.42) 236.7 (181.3e316.8) 37.8 (37.0e38.9) 209.5 (86.2e315.9) 45.1 (38.6e50.3) 34.6 (17.4e52.6) 94.8% (87.9%e98.2%) 21.4% 32.9%

47.1 (31.2e59.6) 57.6% 21.7 (17.9e25.8) 10.72 (7.14e15.38) 7.45 (5.79e9.62) 2.10 (1.92e2.39) 222.1 (154.1e296.0) 38.1 (37.0e39.3) 198.4 (103.5e306.2) 43.6 (37.2e48.3) 41.9 (19.2e57.0) 92.1% (88.4%e98.7%) 27.3% 27.2%

46.3 (32.4e54.9) 65.6% 23.9 (19.4e26.7) 13.51 (8.39e17.33) 7.59 (6.05e9.71) 2.06 (1.90e2.38) 199.3 (152.8e301.4) 38.1 (36.9e39.1) 223.4 (107.2e329.1) 45.6 (37.8e51.4) 36.0 (14.8e49.7) 96.3% (90.2%e99.1%) 26.8% 25.0%

training data are used to optimize the number of neurons in a trialand-error process for improving accuracy which resulted in nine neurons [16]. The output of hidden layer neurons corresponding to P input of output layer neurons may be displayed as sðlÞ ¼ R1 j¼1 Vði;jÞ *yðjÞ  b2ðlÞ; oðlÞ ¼ f ðsðlÞÞ;l ¼ 1; 2;/;R3, In the formula, Vði;jÞis the connection weight between output layer neurons (l) and hidden layer neurons (j), b2ðlÞis the threshold of output layer neurons. With respect to the number of hidden units, a pruning method is used to eliminate the weights which are lower than the threshold value (0.5) of input and hidden units at the end of the training process to obtain fast networks with equivalent performance. The output layer consists of one neuron representing the occurrence of ALI (valued as 1 for the positive response, and 0 for the negative response). The train rate, validation rate and test rate were set at

P-value

0.576 0.213 0.464 0.306 0.136 0.282 0.172 0.377 0.161 0.695 0.083 0.112 0.248 0.197

about 64%, 18% and 18% respectively (by a 7:2:2 ratio). The termination criterion was set at 100 cycles and the models all converged before the 100 cycles. To avoid over-training and to enhance the generalization ability, a weight decay term is used [17]. The calibration of the model is carried out by using a five-fold cross-validation procedure [18]. Logistic regression modeling Logistic regression modeling is used to predict the likelihood of occurrence of ALI following to SAP as a kind of generalized linear regression model; the combination of each predictor is used to predict ALI by a link function, log{E(Y)/[1-E(Y)]} ¼ ¼ Pnk¼1 bkXk, where Y indicated the event. E(Y) is the expected probability of occurrence of ALI, Xk and bk indicated the kth predictor and its

Fig. 2. BP artificial neural networks performance plot with 9 neurons in the hidden layer showing training, validation and testing data set in terms of mean square error.

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Fig. 3. BP artificial neural networks regression plot with 9 neurons in the hidden layer showing training, validation, testing and overall regression values.

weight coefficient in the model [19,20]. The data-set is randomly separated into a training set and a test set by a 1:1 ratio. The training set is applied to construct the logistic regression model. As same as ANNs modeling, initial set of input variables are used in logistic regression modeling. For the output, a binary variable is applied with one category implying a patient. with a positive result (1) and others implying negative result (0). Statistical analysis Continuous data are represented as Medians (interquartile ranges). Significant differences between different groups are evaluated by chi-squared analysis and unpaired Student t-test. Logistic regression analysis and ANN models are developed to predict the occurrence of ALI in patients with SAP. The ANN models are performed with Matlab2017a (MathWorks Institute, USA). Statistical analyses are performed by SPSS 17.0 software (SPSS, Chicago, IL). Prediction indicators include sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and accuracy. P values < 0.05 are considered statistically significant. Dichotomous variables are created out of continuous variables by using clinically important cut off points. Receiver operating characteristic (ROC) curves for models are used to measure the distinction of the models also using Matlab2017a.

Results BP artificial neural networks analyses The training dataset is used to construct BP neural networks model. Characteristics of the training, validation and test data-set are summarized in Table 1. The three data sets are not different significantly for any of the 14 variables (P > 0.05), it means that the three data sets are well balanced in the distribution of clinical characteristics. BP ANNs performance plot showed best validation performance is at epoch 61 with mean squared error 0.0023703 (Fig. 2). Training, validation, testing and overall regression values show the ANNs model is ideal relatively according to Fig. 3. From the training BP ANNs model, we find that, PNR, LDH and SaO2% are the important factors among all thirteen independent variables for ALI, the normalize importance of them are 100%, 90.7% and 84.1% respectively (Fig. 4). When the BP ANNs model applied to the test set, it reveals the SEN of 87.5%, the SPE of 83.3% and the accuracy of 84.4% (Table 2). In our study, we compare the accuracy for predicting ALI and non-ALI by cumulative gains chart, the result shows that the degree of fitting in ALI is better than that of non-ALI, that is to say, the BP ANNs model is more suitable to predict ALI than nonALI (Fig. 5).

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Fig. 4. The importance of variables in predicting acute lung injury following to acute pancreatitis in ANNs model.

Table 2 Comparison of the ANNs model and logistic regression model for predicting ALI following to SAP in test set. Variable

ANNs model

logistic regression model

Difference between models (95%CI)

P-value

Sensitivity Specificity PPV NPV Accuracy AUC

87.5% 83.3% 63.6% 95.2% 84.4% 0.859 ± 0.048

75.0% 70.8% 46.2% 89.5% 71.9% 0.701 ± 0.041

12.5% (3.7%e20.6%) 12.5% (5.7%e21.9%) 17.4% (1.8%e29.3%) 5.7% (1.9%e20.7%) 12.5% (6.8%e20.3%) 0.158 (0.037e0.377)

0.031 0.027 0.006 0.090 0.023 0.012

Abbreviation: ANNs, artificial neural networks; ALI, acute lung injury; SAP, severe acute pancreatitis; PPV, positive predictive value; NPV, negative predictive value; AUC, area under ROC(Receiver Operating Characteristic) curve.

Logistic regression analyses Univariate logistic regression analysis identifies thirteen relevant factors of ALI (Table 3). In these factors, age, LDH, CRP and PNR demonstrates difference between ALI patients and non-ALI patients significantly (P＜0.05). When age, LDH, CRP and PNR are brought into multivariate logistic regression, the four variables are identified significant correlated with ALI (P＜0.05) (Table 3). Then the four variables are included in the multivariate logistic regression analysis as the predictor variables. At a classification threshold of 0.5, when the logic regression model is applied to the test set, it has a SEN of 75.0%, a SPE of 70.8% and the accuracy was 71.9% (Table 2). Comparisons between two models The evaluation indexes of the BP ANNs model and logistic regression model are compared (Table 2). There are significant differences between the two models in SEN, SPE and accuracy (P＜ 0.05). Area under receiver operating characteristic curves (AUC) value are obtained from the logistic regression and ANNs model constructed using the test data set for identifying ALI. AUC value of ANNs model is 0.859 ± 0.048 (95% CI: 0.796e0.947), which shows

more accurate overall performance than the logistic regression model 0.701 ± 0.041 (95% CI: 0.664e0.857). (P ¼ 0.012). Discussion SAP is defined as the presence of persistent organ dysfunction, ie. pulmonary, renal, or cardiovascular. SAP can also lead to many severe complications including ALI. Early identification of ALI disease allows identification of patients who should be monitored and managed in a critical care facility and may help to identify patients who would benefit from transfer to a specialized unit [21,22]. Clinical assessment alone is a poor predictor of ALI following to SAP [23]. The dilemma has lead to the development and utilization of some biochemical markers, or computational models for the purpose. However these methods aren't ideal for clinical predictions [24e26]. Logistic regression is a widely used statistical modeling technique in which the probability of an outcome is related to a series of potential predictor variables. However, logistic regression models require more formal statistical training to develop, they can't implicitly detect complex nonlinear relationships between independent and dependent variables, and they don't have the ability to detect all possible interactions between predictor variables. The outcome of this study demonstrates that the ANNs model is

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Fig. 5. Cumulative gains chart of ANNs model predicting acute lung injury (ALI) following to acute pancreatitis. (“0” means ALI, “1” means non-ALI).

Table 3 Univariate and multivariate analysis of factors affecting acute lung injury following to severe acute pancreatitis. Univariate analysis Variable Age (year) Gender (man%) BMI WBC (  109/L) FBG (mmol/L) [Ca2þ] (mmol/L) AMY (Unit) Temp ( C) LDH (IU/L) Alb (g/L) CRP (㎎/L) SaO2% PNR

ALI Medians (IQR) 51.3 (37.3e64.9) 53.6% 0.263 (0.206e0.281) 15.49 (8.14e20.26) 7.63 (4.76e11.86) 2.09 (1.97e2.40) 249.6 (161.5e327.3) 37.9 (37.1e38.6) 267.9 (116.0e328.1) 43.7 (37.3e51.7) 42.1 (22.8e60.1) 91.7% (86.8%e97.6%) 32.6%

multivariate analysis Non-ALI Medians (IQR) 45.7 (32.0e58.1) 58.8% 0.220 (0.203e0.274) 11.87 (7.36e17.55) 7.58 (4.22e10.67) 2.14 (1.96e2.42) 228.4 (150.3e298.7) 38.2 (37.3e39.1) 201.7 (83.6e298.2) 45.6 (37.5e50.9) 29.7 (18.5e53.0) 94.3% (87.1%e98.9%) 21.3%

P-value 0.036 0.339 0.152 0.071 0.375 0.262 0.187 0.283 0.011 0.195 0.003 0.086 0.007

B 0.026 e e e e e e e 0.842 e 2.986 e 2.469

S.E. 0.017

Sig. 0.058

Exp(B) 0.764

95% CI for Exp(B) 0.547e0.839

0.054

0.010

0.906

0.826e0.966

0.379

0.000

0.975

0.941e0.998

0.513

0.002

0.938

0.879e0.991

IQR: interquartile ranges; ALI: acute lung injury; BMI: body mass index; WBC: white cell count; FBG: fasting blood glucose; AMY: serum amylase; Temp: temperature; LDH: lactic dehydrogenase; Alb: serum albumin; CRP: c reactive protein; SaO2%: oxyhemoglobin saturation; PNR: pancreatic necrosis rate.

more likely to predict the occurrence of ALI following to AP. ANNs have been used as predictive models in other disease conditions, notably in patients with cancer where they have been found to be superior to conventional predictive models even when the same input variables are used to create the ANN. Compared with logistic regression models, ANNs is superior to logistic regression in the SEN, SPE, PPV and accuracy of predicting the occurrence of ALI following to SAP. The AUC value for identifying ALI using the ANNs model was higher than logistic regression model, which illustrated

the more accurate prediction of ANNs model in ALI. Compared with logistic model, An Ann can construct more complex nonlinear relationships, because the predictor variables in ANN usually undergo a nonlinear transformation at each hidden node and output node. Empirical observations suggest that when complex nonlinear relationships exist in data sets, neural network models may provide a tighter model fit than conventional regression techniques [27,28]. ANNs model performed nearly as well as the logistic regression models but required considerably less effort

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to generate. ANNs algorithm automatically handles missing values and performs automatic feature selection; preprocessing or statistical analysis of the data wasn't necessary. Meanwhile, we reveals that four factors including PNR, LDH and SaO2% are important independent variable for ALI, which is consistent with other reports [2,6]. It means that clinicians might monitor or intervene these indicators to prevent the formation of ALI in the process of treatment. In our study, the accuracy predicting ALI and non-ALI were compared by ANNs model, the result shows that the ANNs model is more suitable to predict ALI than non-ALI. It would be more benefit to reduce misdiagnosis. When the clinicians detect a patient that will likely develop ALI according to our ANN-model, they should try to prevent atelectasis, reduce excessive ventilation, increase supplements of oxygen and fluid recovery. Some prophylactic medicine may be considered to use such as aspiration, corticosteroids, heparin (inhalation), peroxisome proliferator agonist receptor (PPAR). The use of neuromuscular blocking agents and blood transfusion should be avoided as much as possible. In our study, we set some important variables including BMI, Ca2þ, AMY, CRP, LDH, PNR etc as the input layers, they are related to the severity of acute pancreatitis [29], which are more likely to cause respiratory failure. Meanwhile, we added SaO2% to train the ANNs model, because it can be used to evaluate and predict the pulmonary function. The pathological anatomy of patients may be changed with age, such as increasing chest hardness, decreasing lung compliance, weakening respiratory muscle, the above can result in abnormal cough reflex, abnormal bronchial cilia swing, and diminishing respiratory membrane's defenses. Some studies showed cytokines could be regarded as the predictors, however these cytokines can't be used for clinical examination in our hospital widely, then we don't set them as input layer. In the future, more valuable factors may be incorporated into ANNs model to improve its predictive ability through prospective studies. Some factors are related to ALI. An example is the degree of necrosis in pancreas, which is related to the occurrence of ALI. Some studies reported that convolution neural network can be used for imaging area assessment, but the technical aspects still need to be improved [30,31]. In our study, evaluation for quantitative indicator of pancreatic necrosis is based on CT examination, the actual area and degree of necrosis isn't easy to measure or calculate, the results may not be accurate. Furthermore, more molecular variables may be included in the ANNs model to improve its predictive ability. In addition to adding new potential variables, there are other features that can be regarded for future developments of ANNs model, such as collecting data prospectively and using a larger sample to avoid possible bias in many factors. Furthermore, the present study being retrospective, there were missing values about 10.0% in some of the analyzed variables, though most input factors have none missing and any missing data are handled using multiple attribution techniques. Data attribution has been shown to be superior to complete case analysis and the missing indicator method in multivariable models [32]. ANNs are suffered from generalization because of the over-fitting data. To improve the generalization, an internal crossvalidation is performed; however external validation would have been beneficial [33]. Conclusion Our study develop and validate a novel ANNs model to screen for SAP patients at risk for ALI at the time of initial emergency department assessment or ICU admission. Our prediction model will facilitate the identification of SAP patients who can benefit from interventions to prevent ALI progression. In order to get more accurate predictions, further optimization of the model have to be performed.

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Conflicts of interest There are no conflicts of interest to be declared. Contributors Yang Fei and Wei-qin Li conceived and designed the study, interpreted the data and wrote the paper. Kun Gao contributed to acquisition and local preparation of the constituent database. Yang Fei carried out the analysis of the data and the ANNs model. Yang Fei contributed to database creation and standardization, design of statistical analyses. Acknowledgement This study was supported by Nanjing University Innovation and Creative Program for PhD candidate(No.CXCY17-29). References [1] Rubenfeld GD, Caldwell E, Peabody E, et al. Incidence and outcomes of acute lung injury. N Engl J Med 2005;353:1685e93. [2] Pastor CM, Matthay MA, Frossard JL. Pancreatitis-associated acute lung injury: new insights. Chest 2003;124:2341e51. [3] Dombernowsky Tl, Kristensen MØ1, Rysgaard S, et al. Risk factors for and impact of respiratory failure on mortality in the early phase of acute pancreatitis. Pancreatology 2016;16:756e60. [4] Landahl P, Ansari D, Andersson R. Severe acute pancreatitis: gut barrier failure, systemic inflammatory response, acute lung injury, and the role of the mesenteric lymph. Surg Infect 2015;16:651e6. [5] Skouras C, Davis ZA, Sharkey J, et al. Lung ultrasonography as a direct measure of evolving respiratory dysfunction and disease severity in patients with acute pancreatitis. HPB 2016;18:159e69. [6] Akbarshahi H, Rosendahl AH, Westergren-Thorsson G, et al. Acute lung injury in acute pancreatitis–awaiting the big leap. Respir Med 2012;106:1199e210. [7] Bonjoch L, Casas V, Carrascal M, et al. Involvement of exosomes in lung inflammation associated with experimental acute pancreatitis. J Pathol 2016;240:235e45. [8] Jiang F, Dong L, Dai Q. Electrical resistivity imaging inversion: an ISFLA trained kernel principal component wavelet neural network approach. Neural Network 2018;104:114e23. [9] McAllister P, Zheng H, Bond R, et al. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets. Comput Biol Med 2018;95:217e33. [10] Takase T, Oyama S, Kurihara M. Effective neural network training with adaptive learning rate based on training loss. Neural Network 2018;101: 68e78. [11] Eslamizadeh G, Barati R. Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods. Artif Intell Med 2017;78:23e40. [12] Fei Y, Hu J, Li WQ, et al. Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis. J Thromb Haemostasis 2017;15:439e45. [13] Valim IC, Fidalgo JLG, Rego ASC, et al. Neural network modeling to support an experimental study of the delignification process of sugarcane bagasse after alkaline hydrogen peroxide pre-treatment. Bioresour Technol 2017;243: 760e70. [14] Banks PA, Bollen TL, Dervenis C, et al. Acute Pancreatitis Classification Working Group. Classification of acute pancreatitis-2012: revision of the Atlanta classification and definitions by international consensus. Gut 2013;62: 102e11. [15] Bernard GR, Artigas A, Brigham KL, et al. Report of the American-European consensus conference on ARDS: definitions, mechanisms, relevant outcomes and clinical trial coordination. The Consensus Committee. Intensive Care Med 1994;20:225e32. [16] Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination insurvival analysis: model specific population value and confidence interval estimation. Stat Med 2004;23:2109e23. [17] Cross SS, Harrison RF, Kennedy RL. Introduction to neural networks. Lancet 1995;346:1075e9. [18] Andersson B, Andersson R, Ohlsson M, et al. Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks. Pancreatology 2011;11:328e35. [19] Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inf 2002;35:352e9. [20] Zhu J, Hastie T. Kernel logistic regression and the import vector machine. J Comput Graph Stat 2005;14:185e205. [21] Meyerholz DK, Williard DE, Grittmann AM, et al. Murine pancreatic duct ligation induces stress kinase activation, acute pancreatitis, and acute lung

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Please cite this article in press as: Fei Y, et al., Artificial neural network algorithm model as powerful tool to predict acute lung injury following to severe acute pancreatitis, Pancreatology (2018), https://doi.org/10.1016/j.pan.2018.09.007