Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the
pancreas. Early identification of patients at high risk for developing a severe course
of the disease is crucial for preventing organ failure and death. Most of the former
predictive scores require many parameters or at least 24 h to predict the severity;
therefore, the early therapeutic window is often missed.The early achievable severity
index (EASY) is a multicentre, multinational, prospective and observational study
(ISRCTN10525246). The predictions were made using machine learning models. We used
the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated
our models using fourfold cross-validation, and the receiver operating characteristic
(ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated
on the union of the test sets of the cross-validation. The most critical factors and
their contribution to the prediction were identified using a modern tool of explainable
artificial intelligence called SHapley Additive exPlanations (SHAP).The prediction
model was based on an international cohort of 1184 patients and a validation cohort
of 3543 patients. The best performing model was an XGBoost classifier with an average
AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience.
The six most influential features were the respiratory rate, body temperature, abdominal
muscular reflex, gender, age and glucose level. Using the XGBoost machine learning
algorithm for prediction, the SHAP values for the explanation and the bootstrapping
method to estimate confidence, we developed a free and easy-to-use web application
in the Streamlit Python-based framework (http://easy-app.org/).The EASY prediction
score is a practical tool for identifying patients at high risk for severe AP within
hours of hospital admission. The web application is available for clinicians and contributes
to the improvement of the model.
