Clinical Dataset for the Paper 'Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis'

This dataset accompanies the manuscript titled "Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis." It includes clinical data used for training and evaluating the machine learning models described in the paper. The dataset contains baseline clinical data of 154 RA patients who were treated with bDMARDs. The labels for remission, and effectiveness (remission and low disease activity) were applied after a 6-month follow-up based on EULAR criteria on DAS28ESR. The sustained effectiveness label indicates maintaining effectiveness within 6 months after initially achieving effectiveness.

Crossponder Authors:

Fatemeh Salehi (email: [e-mail])

本数据集配套于题为《类风湿关节炎（Rheumatoid Arthritis, RA）患者对生物类改善病情抗风湿药（biological disease-modifying antirheumatic drugs, bDMARDs）治疗响应的机器学习预测》的研究手稿。本数据集包含用于训练与评估该论文所述机器学习模型的临床数据，涵盖154名接受bDMARDs治疗的RA患者的基线临床资料。
在治疗后6个月随访阶段，基于欧洲抗风湿病联盟（European League Against Rheumatism, EULAR）制定的血沉28关节疾病活动度评分（Disease Activity Score 28 with Erythrocyte Sedimentation Rate, DAS28ESR）标准，完成了疾病缓解与治疗有效性（包含疾病缓解及低疾病活动度）两类标签的标注。其中，持续有效性标签指患者在初始获得治疗有效性后，于后续6个月内维持该有效性状态。

通讯作者：
Fatemeh Salehi（电子邮箱：[e-mail]）