| 查青林,何羿婷,喻建平,闫小萍,苏励,宋跃进,曾升平,刘维,冯兴华,钱先,朱婉华,林色奇,吕诚,吕爱平.基于决策树分析方法探索类风湿性关节炎证病信息与疗效的相关关系[J].,2006,(10):871-876 |
| 基于决策树分析方法探索类风湿性关节炎证病信息与疗效的相关关系 |
| Correlations between Diagnostic Information and Therapeutic Efficacy in Rheumatoid Arthritis Analyzed with Decision Tree Model |
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| DOI： |
| 中文关键词: 诊断信息 决策树分析 数据挖掘 类风湿性关节炎 |
| 英文关键词:diagnostic information decision tree analysis data mining rheumatoid arthritis |
| 基金项目:国家自然科学基金重大计划重点项目(No.90209002);国家十五攻关计划(No.2001BA701A17);国家自然科学基金项目(No.3042121);上海高校中医内科E-研究院项目(No.E03008) |
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| 中文摘要: |
| 目的利用决策树模型分析方法探索类风湿性关节炎(rheumatoid arthritis,RA)证候疾病信息与疗效的关系。方法397例确诊活动期RA病例来自9个临床中心,随机分成中药治疗组203例和西药治疗组194例。西药治疗方案包括非甾体抗炎药和慢作用药,中药治疗包括基础治疗和辨证用药。治疗前后收集病人中医四诊信息和西医疾病诊查指标,治疗效果用ACR20判断,抽取患者初诊时的信息进行分析,分析方法在SAS8·2上实施。通过单因素探索性分析,计算疗效与变量的比数比,以P<0·2作为入选模型的标准;采用决策树进行挖掘分析。以疗效为分层变量,随机将数据集分为训练集(占75%)和验证集(占25%),对分析方法进行验证。结果数据分析模型中,中药治疗组共纳入变量20个,西药治疗组纳入变量26个。中药组中晨僵、关节肿胀数、IgM、关节压痛数、关节压痛、类风湿因子、C反应蛋白、关节疼痛、医生总体评价与疗效呈正相关,病程和夜尿多与疗效呈负相关。西药治疗组中有11项观测指标与疗效相关,其中血沉、腰膝酸软、舌苔白、关节疼痛、屈伸不利、医生总体评价、关节肿胀、患者总体评价等8项指标与疗效呈正相关,舌苔黄、舌红、白细胞检测与疗效呈负相关。决策树分析结果显示:中药治疗组中晨僵、舌淡红、关节压痛程度、夜尿多4项观测指标不同组合患者的中药治疗疗效有差异;西药组中舌苔白、C反应蛋白、白细胞数量和晨僵4项观测指标不同组合患者的中药治疗疗效有差异;同时,决策树分类的结果在随机选取的验证集中也得到了验证。结论利用决策树分析方法分析证病信息与中西医疗法疗效的关系,符合中医辨证论治个体化诊疗思想,有利于提高治疗方案使用的针对性。 |
| 英文摘要: |
| Objective To explore the correlations between diagnostic information and therapeutic efficacy in rheumatoid arthritis (RA) with decision tree model analysis. Methods Three hundred and ninety seven patients came from 9 clinical centers were randomly divided into the Western medicine (WM) group (n[WTBZ]=194) treated with non-steroidal anti-inflammatory drugs and slow-acting antirheumatic drug and the Chinese medicine (CM) group (n=203) with basic therapy and syndrome-differentiation dependant TCM treatment. TCM and WM diagnostic information were collected. The ACR 20 was used for efficacy evaluation and the information of patients before treatment was analyzed by SAS 8.2 statistical package. Through single-factor exploratory analysis, odds ratio of efficacy and variable was calculated taken P<0.2 as the including criteria for data mining analysis with decision tree model. All data were classified into the training set (75%) and verifying set (25%) with efficacy as the variable for layering to make further verification of the data-mining analysis. Results Twenty variables were included in the CM group and 26 in the WM group in the data-mining model. In the former, 9 variables were positively correlated to the efficacy, including degree of arthralgia, tenderness and morning stiffness, number of swollen joint, and joint with tenderness, levels of IgM, rheumatoid factor (RF), C-reactive protein (CRP), and total assessment from doctor; and disease duration and degree of nocturnal polyuria were negatively correlated to that. While in the latter, 8 were positively correlated to the efficacy, including erythrocyte sedimentation rate (ESR), sour and weak waist and knees, white fur in tongue, joint ache and stiffness, swollen joint, and total assessment from doctor and patient, and red tongue with yellow fur and leucocyte count negatively correlated to it. Data mining with decision tree analysis revealed that different combinations of morning stiffness, slight red tongue, joint tenderness and nocturnal polyuria in the CM group, and those of white fur in tongue, CRP level, leucocyte count and morning stiffness in the WM group showed different efficacy, which were also verified in the randomly chosen verifying set. Conclusion To analyze the correlations between diagnostic information and therapeutic efficacy with decision tree analysis is conformed to the theory of TCM in applying treatment according to syndrome differentiation individually, thus it would contribute to elevate the accuracy of therapy. |
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