University of Pécs Medical School Research Fund(300909)
(Artificial Intelligence National Laboratory Programme) Támogató: NKFIH
MILAB(RRF-2.3.1-21-2022-00004) Támogató: NKFIH
Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP).
However, the clinical scores currently in use are either too complicated or require
data that are unavailable on admission or lack sufficient predictive value. We therefore
aimed to develop a tool to aid in necrosis prediction. The XGBoost machine learning
algorithm processed data from 2387 patients with AP. The confidence of the model was
estimated by a bootstrapping method and interpreted via the 10th and the 90th percentiles
of the prediction scores. Shapley Additive exPlanations (SHAP) values were calculated
to quantify the contribution of each variable provided. Finally, the model was implemented
as an online application using the Streamlit Python-based framework. The XGBoost classifier
provided an AUC value of 0.757. Glucose, C-reactive protein, alkaline phosphatase,
gender and total white blood cell count have the most impact on prediction based on
the SHAP values. The relationship between the size of the training dataset and model
performance shows that prediction performance can be improved. This study combines
necrosis prediction and artificial intelligence. The predictive potential of this
model is comparable to the current clinical scoring systems and has several advantages
over them.
