Journal Article

EASY‐APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

Vincze, Áron, Molontay, Roland, Galeev, Shamil, Bajor, Judit, de‐Madaria, Enrique, Németh, Balázs Csaba, Váncsa, Szilárd, Hegyi, Péter, Török, Imola, Pintér, József, Fehér, Eszter, Farkas, Nelli, Vitális, Zsuzsanna, Gede, Noémi, Földi, Ildikó, Alberti, Piero, Farkas, Hunor, Latawiec, Diane, Czimmer, József, Pár, Gabriella, Szatmary, Peter, Kovács, György, Gajdán, László, Nagy, Marcell, Sallinen, Ville, Fejes, Roland, Gódi, Szilárd, Papp, Mária, Ramírez‐Maldonado, Elena, Illés, Anita, Kui, Balázs, Halloran, Chris, Hágendorn, Roland, Párniczky, Andrea, Sutton, Robert, Márta, Katalin, Pando, Elizabeth, Hegyi, Péter Jenő, Tantau, Alina, Mickevicius, Artautas, Szentesi, Andrea, Szabó, Imre, Sarlós, Patrícia, Erőss, Bálint, Izbéki, Ferenc, Gómez‐Jurado, Maria José

2022

Overview

Background 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. Methods 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). Results 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/). Conclusions 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. The EASY prediction score is a practical tool for identifying patients at a greater risk for severe acute pancreatitis shortly after hospital admission. The explanation of the impact of features on the prediction helps physicians understand the decision of the machine learning model. The easy‐to‐use web application is available for clinicians and contributes to the improvement of the model.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: acute pancreatitis / Algorithms / Artificial intelligence / Classification / Clinical medicine / Cysts / Data collection / Data science / Datasets / Disease / Hospitals / Laboratories / Machine learning / Missing data / Mortality / Pancreatitis / Patients / severity prediction