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Abstract：
The aim of this study was to develop and validate predictive models for assessing the risk of death in patients with acute diquat (DQ) poisoning using innovative machine learning techniques. Additionally, predictive models were evaluated through the application of SHapley Additive ExPlanations (SHAP). A total of 201 consecutive patients from the emergency departments of the First Hospital and Shengjing Hospital of China Medical University admitted for deliberate oral intake of DQ from February 2018 to August 2023 were analysed. The initial clinical data of the patients with acute DQ poisoning were collected. Machine learning methods such as logistic regression, random forest, support vector machine (SVM), and gradient boosting were applied to build the prediction models. The whole sample was split into a training set and a test set at a ratio of 8:2. The performances of these models were assessed in terms of discrimination, calibration, and clinical decision curve analysis (DCA). We also used the SHAP interpretation tool to provide an intuitive explanation of the risk of death in patients with DQ poisoning. Logistic regression, random forest, SVM, and gradient boosting models were established, and the areas under the receiver operating characteristic curves (AUCs) were 0.91, 0.98, 0.96 and 0.94, respectively. The net benefits were similar across all four models. The four machine learning models can be reliable tools for predicting death risk in patients with acute DQ poisoning. Their combination with SHAP provides explanations for individualized risk prediction, increasing the model transparency.
摘要：
这项研究的目的是开发和验证预测模型，以使用创新的机器学习技术评估急性敌快（DQ）中毒患者的死亡风险。此外,预测模型通过应用SHapley添加剂explanations（SHAP）进行评估。对2018年2月至2023年8月期间收治的201例故意口服DQ的中国医科大学附属第一医院和盛京医院急诊科患者进行分析。收集急性DQ中毒患者的初步临床资料。逻辑回归等机器学习方法，随机森林,支持向量机(SVM)，并应用梯度提升来建立预测模型。将整个样品以8:2的比率分成训练集和测试集。这些模型的性能是根据歧视进行评估的，校准,和临床决策曲线分析(DCA)。我们还使用SHAP解释工具对DQ中毒患者的死亡风险提供了直观的解释。Logistic回归，随机森林,SVM,并建立了梯度增强模型，受试者工作特征曲线下面积(AUC)分别为0.91、0.98、0.96和0.94。所有四个模型的净收益相似。这四种机器学习模型可以成为预测急性DQ中毒患者死亡风险的可靠工具。他们与SHAP的结合为个性化风险预测提供了解释，增加模型的透明度。
