Artificial Intelligence Prediction for Severity of Acute Pancreatitis
Artificial Intelligence Prediction for Severity of Acute Pancreatitis
The incidence of acute pancreatitis (AP) is increasing nowadays. The diagnosis of AP is
 defined according to Atlanta criteria with the presence of two of the following 3 findings
 (1); a) characteristic abdominal pain b) amylase and lipase values ≥3 times c) AP diagnosis
 in ultrasonography (USG), magnetic resonance imaging (MRI), or computerized tomography (CT)
 imaging. While 80% of the disease has a mild course, 20% is severe and requires intensive
 care treatment. Mortality varies between 10-25% in severe (severe) AP, while it is 1-3% in
 mild AP.

 Scoring systems with clinical, laboratory and radiological findings are used to evaluate the
 severity of the disease. Advanced age (>70yo), obesity (as body mass index (BMI, as kg/m2),
 cigarette and alcohol usage, blood urea nitrogen (BUN) ≥20 mg/dl, increased creatinine, C
 reactive protein level (CRP) >120mg/dl, decreased or increased Hct levels, ≥8 Balthazar score
 on abdominal CT implies serious AP. According to the revised Atlanta criteria, three types of
 severity are present in AP. Mild (no organ failure and no local complications), moderate
 (local complications such as pseudocyst, abscess, necrosis, vascular thrombosis) and/or
 transient systemic complications (less than 48h) and severe (long-lasting systemic
 complications (>48h); organ insufficiencies such as lung, heart, gastrointestinal and renal).
 Although Atlanta scoring is considered very popular today, it still seems to be in need of
 revision due to some deficiencies in the subjects of infected necrosis, non-pancreatic
 infection and non-pancreatic necrosis, and the dynamic nature of organ failure. Even though
 the presence of 30 severity scoring systems (the most accepted one is the APACHE 2 score
 among them), none of them can definitely predict which patient will have very severe disease
 and which patient will have a mild course has not been discovered yet.

 Today, artificial intelligence (machine learning) applications are used in many subjects in
 medicine (such as diagnosis, surgeries, drug development, personalized treatments, gene
 editing skills). Studies on machine learning in determining the violence in AP have started
 to appear in the literature. The purpose of this study is to investigate whether the
 artificial intelligence application has a role in determining the disease severity in AP.
In a retrospective way, 1550 patients who were followed up at the Gastroenterology Clinic of
 Bezmialem Foundation University between October 2010 and February 2020 period and who were
 diagnosed with AP according to Atlanta criteria were screened. After the removal of 360
 patients with missing data, 1189 patients were included in the study for evaluation. Then,
 the study will be evaluated in the other 87 patients' (those admitted between February-August
 2020 period) by unaware of the results to the artificial intelligence operator about outcomes
 such as complications, need for ICU, interventional procedures, etc. So, the total patient
 number is 1267. The data obtained will be obtained from routine file data between 2010 and
 2020.

 1. Patient demographic information; [age (yo), gender (male/female), cigarette/alcohol
 usage (as yes or no)], clinical information; [height (centimeters), weight (kilograms),
 BMI (as kg/m2), presence of diabetes mellitus and hypertension (yes or no)], etiology of
 AP such as gallstones, alcohol, etc., and laboratory tests those taken within the first
 24 hours of the admission; [CRP level (mg/dl, normally: 0-5), BUN level (mg/dl,
 normally; 9,8 - 20,1), creatinine level (mg/dl, normally; 0,57 - 1,11), number of
 leukocytes (normally 4.5 to 11.0 ×109/L) and hematocrit level (%, normally: 35,5-48%)],
 as well as Balthazar tomographic scoring [0: normal, 1: an increase in pancreatic size,
 2: inflammatory changes in pancreatic tissue and peripancreatic fatty tissue, 3:
 irregularly bordered, single fluid collection, 4: irregularly bordered 2 or more fluid
 collections, 5 to 10 different degrees of necrosis)], will be recorded in the excel
 file.

 2. Revised Atlanta scoring will also be recorded within a week period of hospital admission
 as mild, moderate, and severe scores. Infected pancreatic necrosis and sepsis that
 developed during the course of acute pancreatitis will be accepted as severe acute
 pancreatitis due to the inadequacy of some issues in Atlanta scoring. The severity of
 the disease will be evaluated according to the Atlanta scores. And the results of the
 artificial intelligence study will be matched according to the results of Atlanta
 scoring.

 3. Additionally, invasive procedure requirements such as endoscopic ultrasonography (EUS),
 endoscopic retrograde cholangiopancreatography (ERCP), length of hospital stay (as
 days), intensive care unit requirement (present or not), number of future AP attacks (in
 duration after a month of hospital admission), and survival (death, alive) will also be
 recorded.

 It is planned that with the help of machine learning methods, using the data given in the
 first paragraph, whether artificial intelligence (AI) can predict the severity of the
 disease. Atlanta scoring will be used as a guide in predicting how the severity of the
 disease will be. In addition, whether the situations in paragraph 3 will develop or not will
 be evaluated with artificial intelligence. During the machine learning study, patients will
 be grouped as; 70% of the patients (approximately 839) for model training; 15% (approx.179)
 validation, 15% (approx.179) reserved for testing suitable. Since cross-validation will also
 be applied to the model here, the data will also change within itself, and also the
 distribution will be optimized to increase the predictive power. Another 87-person patient
 group will also be used to validate the model hypothesis with data independent of the first
 data set, so it will be the second test data.

 Artificial Intelligence Methods of the Study

 Defining the Problem: The problem is defined as multiple output regression which involves the
 prediction of two or more numerical values using a data set consisting of multiple input
 properties. It is based on the estimation of values of the output variables defined by the
 input set, either in a time-dependent or time-independent fashion. This kind of problem can
 be solved and modeled by machine learning algorithms (linear regression, decision trees,
 etc.) based on the wrapper model, which naturally allows the use of multiple output
 regression.

 Regression describes a predictive modeling problem involving the estimation of a numerical
 value. In multiple-output regression, the outputs are connected to the input and to each
 other. This means that often the outputs are not independent of each other and a model may be
 required that predicts both outputs together or each output based on other outputs.
 Multi-step time series estimation can be thought of as a type of multiple output regression
 where a sequence of future values is predicted and each predicted value depends on previous
 values in the sequence.

 Solution Methodology: Although neural networks are widely known for modeling complex problems
 such as natural language processing, image recognition, they can be easily adapted to
 regression problems. Any class of statistical models can be called a neural network if it
 uses adaptive weights and can approximate nonlinear functions of its inputs.

 Therefore, neural network regression is appropriate for problems for which a more traditional
 regression model cannot provide a suitable solution. Neural network regression is a
 supervised learning method and therefore, it requires a dataset containing a column of
 labels. Since a regression model predicts a numeric value, the label column must be a numeric
 data type. The model and the labeled dataset are fed as input to the training model or
 hyperparameter tuning model. The trained model is then used to predict output values for new
 input samples. The deep neural network regression model is created using the Python-based
 KerasRegressor (TensorFlow-Keras API) library. The deep neural network regression model can
 predict many more parameters and parameter permutations than classical machine learning
 algorithms such as logistic regression. In order to obtain reliable estimates, the ratio of
 data points to parameters should be reasonable and there should be a sufficient number of
 samples.

 "GridSearchCV" is utilized for model hyperparameter fine-tuning (for hyperparameters such as
 dropout rate, regularization rate, momentum and learning rate, etc.). In the "GridSearchCV"
 method, a sequence of values are defined for each hyperparameter, and combinations of these
 values, i.e. a grid of hyperparameters are used to search for the most appropriate
 configuration of hyperparameters. Among a set of neural networks with varying topologies in
 terms of input, output and hidden layers and activation functions, the network model that
 yields the best performance is constructed. In every iteration, a value that represents that
 step is calculated before each correction to adapt the learning rate to a proper value that
 enables as fast convergence as possible while preventing the model from overshooting local
 minima. For training, the maximum number of iterations and batch size is determined. Feature
 normalization (binning, gaussian, min-max, rescaling, etc.) is applied. Samples are shuffled
 between iterations to have a different randomized order of samples at each iteration.

 A seed value, which is an optional value to define random seed is provided. Moreover,
 concerning the neural network architecture, it is possible to adjust visible layers, hidden
 layers, and dropout rates via unsupervised learning algorithms (stacked autoencoder, deep
 belief networks, etc.). Optimal node connection weights are obtained with algorithm
 optimization and fine-tuning of hyperparameters. The loss function is evaluated on the basis
 of "mse", as it is one of the best performance criteria for regression. (Bu cümlenin
 Furthermore, for the minimization of the loss function, an optimization algorithm for
 "backpropagation" weight update (e.g. quasi-Newton limited-memory
 Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), stochastic gradient descent (SGD), adaptive moment
 estimation (Adam), etc.) is utilized.
Inclusion Criteria:

 - Patients with acute pancreatitis diagnosis who admitted to ER within 24 hours after the
 beginning of abdominal pain

 Exclusion Criteria:

 -  Patients who sign a treatment rejection form immediately after admission to the
 hospital and leave the hospital

 -  Patients with uncompleted data

 -  Psychiatric patients

 -  Patients with very poor general conditions