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- W4283330755 abstract "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." @default.
- W4283330755 created "2022-06-24" @default.
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- W4283330755 date "2022-06-20" @default.
- W4283330755 modified "2023-09-27" @default.
- W4283330755 title "A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization" @default.
- W4283330755 doi "https://doi.org/10.48550/arxiv.2206.09752" @default.
- W4283330755 hasPublicationYear "2022" @default.
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