This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area hospital dataset to predict the mortality of COVID-19 patients. We apply two XAI methods, Shapley Additive exPlanations (SHAP) and Locally Interpretable Model Agnostic Explanations (LIME), to compare the global and local interpretation of feature importance. This paper demonstrates the advantages of using XAI which shows the feature importance and decisive capability. Furthermore, we use the XAI methods to cross-validate their interpretations for individual patients. The XAI models reveal that Medicare financial class, older age, and gender have high impact on the mortality prediction. We find that LIME local interpretation does not show significant differences in feature importance comparing to SHAP, which suggests pattern confirmation. This paper demonstrates the importance of XAI methods in cross-validation of feature attributions.

翻译：本文应用可解释人工智能（XAI）方法研究新冠肺炎患者死亡率中的社会经济差异。基于去标识化的奥斯汀地区医院数据集，构建了极端梯度提升（XGBoost）预测模型，用于预测新冠肺炎患者的死亡率。我们采用两种XAI方法——Shapley加性解释（SHAP）和局部可解释模型无关解释（LIME），比较特征重要性的全局与局部解释。本文展示了XAI的优势，揭示了特征重要性及其决策能力。此外，我们利用XAI方法对个体患者的解释进行交叉验证。XAI模型显示，医疗保险财务状况、年龄较大和性别对死亡率预测具有显著影响。我们发现，与SHAP相比，LIME的局部解释在特征重要性上未呈现显著差异，这表明模式一致性。本文论证了XAI方法在特征归因交叉验证中的重要性。