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

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http://hdl.handle.net/10138/345947

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Hungarian Pancreatic Study Grp , Kui , B , Pinter , J & Molontay , R 2022 , ' EASY-APP : An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis ' , Clinical and translational medicine , vol. 12 , no. 6 , 842 . https://doi.org/10.1002/ctm2.842

Title: EASY-APP : An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis
Author: Hungarian Pancreatic Study Grp; Kui, Balazs; Pinter, Jozsef; Molontay, Roland
Contributor organization: Clinicum
HUS Abdominal Center
Pertti Panula / Principal Investigator
Department of Anatomy
University of Helsinki
IV kirurgian klinikka
Date: 2022-06
Language: eng
Number of pages: 15
Belongs to series: Clinical and translational medicine
ISSN: 2001-1326
DOI: https://doi.org/10.1002/ctm2.842
URI: http://hdl.handle.net/10138/345947
Abstract: 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.
Subject: acute pancreatitis
artificial intelligence
severity prediction
APACHE-II SCORE
INTERNATIONAL COHORT
ADMISSION
DISEASE
INDEX
217 Medical engineering
3121 General medicine, internal medicine and other clinical medicine
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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