Predicting Disease Activity for Biologic Selection in Rheumatoid Arthritis

M. Yamauchi, K. Nakano, Yoshiya Tanaka, K. Horio
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Abstract

In this article, we implemented a regression model and conducted experiments for predicting disease activity using data from 1929 rheumatoid arthritis patients to assist in the selection of biologics for rheumatoid arthritis. On modelling, the missing variables in the data were completed by three different methods, mean value, self-organizing map and random value. Experimental results showed that the prediction error of the regression model was large regardless of the missing completion method, making it difficult to predict the prognosis of rheumatoid arthritis patients.
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类风湿关节炎疾病活动性的生物选择预测
在这篇文章中,我们使用1929年类风湿性关节炎患者的数据实施了一个回归模型并进行了预测疾病活动性的实验,以帮助选择治疗类风湿性关节炎的生物制剂。在建模方面,采用均值、自组织映射和随机值三种不同的方法对数据中的缺失变量进行补全。实验结果表明,无论采用何种缺失补全方法,回归模型的预测误差都较大,难以预测类风湿关节炎患者的预后。
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