建立对临床人工智能的信任：一种用于慢性肾脏病的基于网络的可解释决策支持系统。
Building Trust in Clinical AI: A Web-Based Explainable Decision Support System for Chronic Kidney Disease.
作者信息
Mridha Krishna, Wang Ming, Zhang Lijun
机构信息
Department of Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, Cleveland, OH, USA.
出版信息
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:375-384. eCollection 2025.
Chronic Kidney Disease (CKD) is a significant global public health issue, affecting over 10% of the population. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. We developed a Web-Based Clinical Decision Support System (CDSS) for CKD, incorporating advanced Explainable AI (XAI) methods, specifically SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). The model employs and evaluates multiple classifiers: KNN, Random Forest, AdaBoost, XGBoost, CatBoost, and Extra Trees, to predict CKD. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and the AUC. AdaBoost achieved a 100% accuracy rate. Except for KNN, all classifiers consistently reached perfect precision and sensitivity. Additionally, we present a real-time web-based application to operationalize the model, enhancing trust and accessibility for healthcare practitioners and stakeholder.
慢性肾脏病（CKD）是一个重大的全球公共卫生问题，影响着超过10%的人口。及时诊断对于有效管理至关重要。在医疗保健领域利用机器学习在预测诊断方面提供了有前景的进展。我们开发了一个用于CKD的基于网络的临床决策支持系统（CDSS），纳入了先进的可解释人工智能（XAI）方法，特别是SHAP（Shapley值加法解释）和LIME（局部可解释模型无关解释）。该模型采用并评估多个分类器：KNN、随机森林、AdaBoost、XGBoost、CatBoost和极端随机树，以预测CKD。通过测量模型的准确率、分析混淆矩阵统计数据和AUC来评估模型的有效性。AdaBoost达到了100%的准确率。除了KNN之外，所有分类器都始终达到了完美的精度和灵敏度。此外，我们展示了一个基于网络的实时应用程序来实施该模型，增强了医疗从业者和利益相关者的信任和可及性。
Zotero 插件