Research on optimal Traditional Chinese Medicine treatment of knee ostarthritis with data mining algorithms

D. Guo, Jian Li, Gang Zhang, Weixiang Lu, Shaojian Xu, Jun Liu
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引用次数: 1

Abstract

At present, more and more patients suffering from knee OA (Ostarthritis) are treated with complementary and alternative medicine, such as herbal drugs, herbal patches, acupuncture and manipulation etc, as an effective therapy. However, traditional statistical methods data gathered from randomized controlled trials (RCT) which were considered as the golden standard for therapy effectiveness failed to confirm those therapies efficacy. Whether we can accurately predict these therapeutic effects on the basis of a prospective, five-center, parallel-group, randomized controlled trial by means of other innovative ways is the question. According to this question, our team adopted several commonly used data mining algorithms to study it, such as KNN (k-Nearest Neighbor algorithm), j48 (decision tree), ANN (Artificial Neural Network). By means of modeling analysis of the patients' Traditional Chinese Medicine (TCM) symptoms questionnaire, Western Ontario and McMaster Universities Index of OA (WOMAC) total score and SF-36 assessment to predict the therapeutic effect which a patient can achieve after adopting one of those TCM therapies. Then we comprehensively analysed the effect and characteristic of every therapy schedule.
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基于数据挖掘算法的膝性骨关节炎中医优化治疗研究
目前,越来越多的膝关节OA (Ostarthritis)患者采用补充和替代药物治疗,如中药、中药贴片、针灸和手法等,作为一种有效的治疗方法。然而,传统的统计学方法收集的随机对照试验(RCT)数据被认为是治疗效果的黄金标准,无法证实这些治疗的疗效。我们能否在前瞻性、五中心、平行组、随机对照试验的基础上,通过其他创新方法准确预测这些治疗效果是一个问题。针对这个问题,我们团队采用了几种常用的数据挖掘算法进行研究,如KNN (k-Nearest Neighbor algorithm)、j48 (decision tree)、ANN (Artificial Neural Network)。通过对患者中医症状问卷、西安大略和麦克马斯特大学OA指数(WOMAC)总分和SF-36评分进行建模分析,预测患者采用其中一种中医疗法后所能达到的治疗效果。综合分析了各种治疗方案的效果和特点。
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