Zhang Rufa, Zhu Shiqi, Shi Li, Zhang Hao, Xu Xiaodan, Xiang Bo, Wang Min
Department of Gastroenterology, The People's Hospital of Nanchuan, No. 16, Nanda Street, Nanchuan District, Chongqing, 408400, China.
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
BMC Med Inform Decis Mak. 2025 Apr 17;25(1):167. doi: 10.1186/s12911-025-02997-7.
Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of acute pancreatitis (AP), often associated with increased mortality. This study aims to leverage automated machine learning (AutoML) algorithms to create a model for the early and precise prediction of SIRS in AP.
This study retrospectively analyzed patients diagnosed with AP across multiple centers from January 2017 to December 2021. Data from the First Affiliated Hospital of Soochow University and Changshu Hospital were used for training and internal validation, while testing was conducted with data from the Second Affiliated Hospital. Predictive models were constructed and validated using the least absolute shrinkage and selection operator (LASSO) and AutoML. A nomogram was developed based on multivariable logistic regression (LR) analysis, and the performance of the models was assessed through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Additionally, the AutoML model's effectiveness and interpretability were assessed through DCA, feature importance, SHapley Additive exPlanation (SHAP) plots, and locally interpretable model-agnostic explanations (LIME).
A total of 1,224 patients were included, with 812 in the training cohort, 200 in validation, and 212 in testing. SIRS occurred in 33.7% of the training cohort, 34.0% in validation, and 22.2% in testing. AutoML models outperformed traditional LR, with the deep learning (DL) model achieving an area under the ROC curve of 0.843 in the training set, and 0.848 and 0.867 in validation and testing, respectively.
The AutoML model using the DL algorithm is clinically significant for the early prediction of SIRS in AP.
全身炎症反应综合征（SIRS）是急性胰腺炎（AP）常见且严重的并发症，常与死亡率增加相关。本研究旨在利用自动机器学习（AutoML）算法建立一个用于早期精准预测AP患者发生SIRS的模型。
本研究回顾性分析了2017年1月至2021年12月期间多个中心诊断为AP的患者。苏州大学附属第一医院和常熟医院的数据用于训练和内部验证，而测试则使用附属第二医院的数据。使用最小绝对收缩和选择算子（LASSO）和AutoML构建并验证预测模型。基于多变量逻辑回归（LR）分析绘制列线图，并通过受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估模型性能。此外，通过DCA、特征重要性、SHapley加性解释（SHAP）图和局部可解释模型无关解释（LIME）评估AutoML模型的有效性和可解释性。
共纳入1224例患者，其中训练队列812例，验证队列200例，测试队列212例。训练队列中33.7%的患者发生SIRS，验证队列中为34.0%，测试队列中为22.2%。AutoML模型优于传统LR，深度学习（DL）模型在训练集中的ROC曲线下面积为0.843，在验证集和测试集中分别为0.848和0.867。
使用DL算法的AutoML模型对AP患者SIRS的早期预测具有临床意义。
