Artificial Intelligence Prediction for the Severity of Acute Pancreatitis
Condition: Acute Pancreatitis; Artificial Intelligence; Outcome, Fatal
MeSH terms: Pancreatitis
Study type: Observational

Summary: 
      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;
      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 (AI) application has a role in determining the disease severity in
      AP.
    

Detailed description: 
      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 216
      patients with missing data, 1334 patients were included in the study for evaluation.

        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. Complications are classified as 0; none, 2; local complications: pseudocyst, abscess,
           necrosis, thrombosis, and mesenteric panniculitis, 3; systemic complications: lung,
           kidney, gastrointestinal and cardiovascular complications, 4; mixed serious
           complications/co-morbidity situations, 5: infectious and septic complications.

        4. Additionally, invasive procedure requirements such as endoscopic ultrasonography (EUS),
           endoscopic retrograde cholangiopancreatography (ERCP) (as yes or no), length of hospital
           stay (less than 10 days or more than 11 days), intensive care unit requirement (present
           or not), number of future AP attacks (in duration after a month of hospital admission,
           as of one attack or more than one attack), and survival (death, alive) will also be
           recorded.

      Machine Learning Algorithm is used: Gradient Boosted Ensemble Trees Trees. ("Greedy Function
      Approximation: A Gradient Boosting Machine" by Jerome H. Friedman (1999)). The dataset has
      been partitioned with a 90%-10% ratio. 10% is for validation and 90% is for AI machine
      learning. 90% machine learning part has also been divided into two parts as 70% for AI
      Learning and 30% for testing the learning. For this purpose, 5-fold stratified sampling has
      been used

      Artificial Intelligence Methods of the Study

      Features Used for AI Machine Learning:

        1. Gender: M/F

        2. Age: Continuous Value

        3. Height (cm): Continuous Value

        4. Weight (Kg): Continuous Value

        5. BMI Groups: Group 1: ≤ 25 kg/m2; Group 2; 25-30 kg/m2; Group 3: >30,1 kg/m2

        6. Cigarette: 0; No, 1; Yes

        7. Alcohol: 0; No, 1; Yes

        8. Diabetes mellitus: 0; No, 1; Yes

        9. Hypertension: 0; No, 1; Yes

       10. Etiology: 1; biliary, 2; Alcohol, 3; hypertriglyceridemia, 4; hypercalcemia, 5; drug, 6;
           congenital, 7; cryptogenic, 8; endoscopic retrograde cholangiography (ERCP), 9; oddy
           sphincter dysfunction (OSD), 10; malignity, 11; intra papillary mucinous neoplasia
           (IPMN), 12: primary sclerosing cholangiography (PSC) 13: autoimmune, 14: multiple
           etiology

       11. Leucocyte number (WBC): N; 4,5-11x100

       12. Hematocrit (Hct): N; %35,5-48

       13. C reactive protein (CRP): N: 0-5 mg/dl

       14. Blood urea nitrogen (BUN): N: 9,8-20,1 mg/dl

       15. Creatinine (KREA): N: 0,57-1,11 mg/dl

       16. Baltazar Scoring (BLTZR): 0; Normal P, 1; 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, with
           various degrees of necrosis (ranging between 5 and 10)

      In Artificial Intelligence, Decision Tree Models are widely used for supervised machine
      learning. They may depend on the Gini index, gain ratio/entropy, chi-square, regression, and
      so on. In AI they are preferred because they generate understandable rules for humans unlike
      other machine learning algorithms such as Artificial Neural Networks and Support Vector
      Machines. On the other hand, they are considered to be weak learners. That means they are
      highly affected by noise and outliers existing in the data set. In order to go around this
      handicap, models like Random Forest, Ensemble Trees, Gradient Boosting have been developed.

      Random forest and Ensemble trees generate rules by applying a certain decision tree algorithm
      to the portions of the data set vertically and horizontally. This technique dramatically
      reduces the error occurring in learning. After learning processes are completed, they combine
      weak decision trees into a strong and bigger decision tree model. Ensemble learning models
      achieve better learning by minimizing the average value of the loss function on the training
      set via a F ̂(x) approximation. The idea is to apply a steepest descent step to the
      minimization problem in a greedy fashion.

      In this study, the gradient boost tree model which was proposed by Friedman has been used for
      machine learning. This model chooses a separate optimal value for each of the tree's parts
      rather than a single one for the whole tree. This approach can be used to minimize any
      differentiable loss L(y, F) in conjunction with forwarding stage-wise additive modeling. It
      is reported that the gradient boosting tree model outperforms random forest and regular
      ensemble trees in many cases.

      The goal of the algorithm is to find an approximation F_m (x_i) which minimizes the expected
      L(y,F(x)) loss function.

      The algorithm may be summarized as follows:

      Inputs:

      A training data set: {(x_i,y_i )} i=1 to n with n dimension and a class variable A
      differentiable loss function: L(y,F(x)) The number of iterations: M.

      Output:

      F_m (x_i)

      Algorithm:

      Initialize the model with a constant value:

      F_0 (x)=arg min⁡∑_(i=1)^n▒〖L(y_i,γ)〗

      For m = 1 to M:

      Compute pseudo-residuals rim r_im=-[(∂L(y_(i,) F(x_i )))/(∂F(x_i))]

      Train a base learner to pseudo-residuals, using the training set:

      {(x_i,y_i )} i=1 to n Compute multiplier γ γ=arg min⁡∑_(i=1)^n▒〖L(y_i,F_(m-1) (x_i )+γh_m
      (x_i ))〗

      Update the model:

      〖F_m (x_i)=F〗_(m-1) (x_i )+γ_m h_m (x_i ) Output F_m (x_i)

      In the analysis, Synthetic Minority Oversampling Technique (SMOTE) [5] has been used in order
      to avoid the disadvantage of class variable imbalance. SMOTE is a data augmentation technique
      to increase data. In some cases, the class variable may not have an equal amount of values
      from all cases. For example, there may be much more survived patients than those who lost
      their lives. In this kind of situation, data are augmented. There was an imbalance in the
      class variables in the data set of this study. So, SMOTE has been applied to increase the
      minority classes for training.

      The dataset has been partitioned with a 90%-10% ratio. 10% is for validation and 90% is for
      AI machine learning. 90% machine learning part has also been divided into two parts as 70%
      for AI Learning and 30% for testing the learning. For this purpose, 5-fold stratified
      sampling has been used. KNIME analytic platform has been used for the AI machine learning.
    

Observational model: Cohort
Time perspective: Retrospective
Sampling method: Non-Probability Sample
Study population: 
        Patients with acute pancreatitis diagnosis according to the Atlanta criteria
      
Keywords: Acute pancreatitis, Artificial intelligence,