班级不平衡学习在预防接种不良事件严重程度预测中应用的比较研究
A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization
摘要：
我们与中国辽宁省疾病预防控制中心合作，提出了一个预测系统，根据免疫接种后不良事件的数据，预测出现不良反应的儿童随后的住院治疗。我们从数据中提取多个特征，并选择“是否住院”作为分类目标。由于数据的不平衡性，我们使用了各种类不平衡学习方法进行训练，并改进了RUSBoost算法。实验结果表明，改进的RUSBoost算法在目标上的ROC曲线下面积最高。此外，我们将这些类不平衡学习方法与一些常见的机器学习算法进行了比较。我们将改进的RUSBoost与动态Web资源开发技术相结合，为相关医疗从业人员构建了一个具有信息录入和疫苗接种反应预测功能的评估系统。
英文摘要：
In collaboration with the Liaoning CDC, China, we propose a prediction system to predict the subsequent hospitalization of children with adverse reactions based on data on adverse events following immunization. We extracted multiple features from the data, and selected "hospitalization or not" as the target for classification. Since the data are imbalanced, we used various class-imbalance learning methods for training and improved the RUSBoost algorithm. Experimental results show that the improved RUSBoost has the highest Area Under the ROC Curve on the target among these algorithms. Additionally, we compared these class-imbalance learning methods with some common machine learning algorithms. We combined the improved RUSBoost with dynamic web resource development techniques to build an evaluation system with information entry and vaccination response prediction capabilities for relevant medical practitioners.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
