使用可解释的人工智能交叉验证Covid-19患者死亡率的社会经济差异

Linlin Shi, Redoan Rahman, E. Melamed, J. Gwizdka, Justin F. Rousseau, Ying Ding
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引用次数: 0

摘要

本文应用可解释人工智能(eXplainable Artificial Intelligence, XAI)方法研究新冠肺炎患者死亡率的社会经济差异。基于去识别的奥斯汀地区医院数据集,建立了极端梯度增强(XGBoost)预测模型,用于预测COVID-19患者的死亡率。我们采用两种XAI方法,Shapley加性解释(SHAP)和局部可解释模型不可知解释(LIME),来比较特征重要性的全局解释和局部解释。本文论证了使用XAI的优势,显示了XAI的特点、重要性和决定性。此外,我们使用XAI方法来交叉验证他们对个体患者的解释。XAI模型显示,医疗保险财务阶层、年龄和性别对死亡率预测有很大影响。我们发现LIME的局部解释在特征重要性上与SHAP没有显著差异,这表明模式得到了确认。本文论证了XAI方法在特征属性交叉验证中的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality
This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID-19 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's 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.
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