1.中国中医科学院中医临床基础医学研究所,北京市东城区东直门内南小街16号,100700
2.华北电力大学数理系
3.上海中医药大学脊柱病研究所
4.上海中医药大学附属龙华医院
王拥军,yjwang8888@126.com;
杨伟,yangyxq@ruc.edu.cn
收稿:2022-08-21,
修回:2023-10-13,
纸质出版:2023-12-17
移动端阅览
栗贝贝,王晶,向兴华等.结合关联规则的隐结构分析构建证候状态辨识模型——以原发性骨质疏松症肾阳虚证为例[J].中医杂志,2023,64(24):2522-2531.
LI Beibei,WANG Jing,XIANG Xinghua,et al.Constructing a Syndrome Differentiation Model by Combining Latent Structure Analysis of Association Rules: An Example of Kidney Yang Deficiency Syndrome in Primary Osteoporosis[J].Journal of Traditional Chinese Medicine,2023,64(24):2522-2531.
栗贝贝,王晶,向兴华等.结合关联规则的隐结构分析构建证候状态辨识模型——以原发性骨质疏松症肾阳虚证为例[J].中医杂志,2023,64(24):2522-2531. DOI: 10.13288/j.11-2166/r.2023.24.008.
LI Beibei,WANG Jing,XIANG Xinghua,et al.Constructing a Syndrome Differentiation Model by Combining Latent Structure Analysis of Association Rules: An Example of Kidney Yang Deficiency Syndrome in Primary Osteoporosis[J].Journal of Traditional Chinese Medicine,2023,64(24):2522-2531. DOI: 10.13288/j.11-2166/r.2023.24.008.
目的
2
以原发性骨质疏松症(POP)肾阳虚证为例,构建中医证候状态定量化辨识模型,为证候诊断标准化研究提供方法学参考。
方法
2
采用描述统计分析筛选出POP高频临床特征,通过关联规则算法得到POP强关联特征。在此基础上结合隐结构分析法建立隐结构(隐树)模型,对与POP肾阳虚证相关隐变量和显变量(特征)进行综合聚类,通过互信息与累积信息覆盖度等指标对聚类结果进行诠释,探究其主症、次症,根据各隐类中特征出现概率推断POP肾阳虚证状态类别。依据状态类别计算临床特征分值和辨识阈值,结合综合判断规则初步构建POP肾阳虚证状态辨识模型,最后以中医专业人员判断结果为金标准进一步评价模型的辅助辨识效果。
结果
2
得到POP强关联的32个特征,进一步构建的隐树模型贝叶斯信息准则(BIC)评分为
<math id="M1"><mo>-</mo><mn mathvariant="normal">15291.93</mn></math>
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16.93333435
2.70933342
。根据互信息和累积信息覆盖度推出POP肾阳虚证的主症为骨软、神疲、舌淡、小便清、夜尿频多、肢冷、脉沉细、苔白,次症为乏力、性欲减退、大便溏、尿频、腰膝酸软、畏寒。由POP肾阳虚证不同隐类中各临床特征出现的概率,推出POP肾阳虚证状态为S0类(无/轻度肾阳虚证)/S1类(中度肾阳虚证)/S2类(重度肾阳虚证)。优化状态辨识初步规则,细化状态S1类,结果显示970例POP患者中,520例患者为无/轻度肾阳虚证、224例为中度偏轻度肾阳虚证、81例为中度偏重度肾阳虚证、145例为重度肾阳虚证。模型评估指标正确率为0.8835、灵敏度为0.7181、特异度为0.9437。
结论
2
结合关联规则的隐结构分析可构建POP肾阳虚证状态辨识模型,且该模型具有良好的定量化辨识效果,可辅助临床医生提高中医诊断效率和准确性。
Objective
2
To construct a quantitative differentiation model of traditional Chinese medicine (TCM) syndromes by taking primary osteoporosis (POP) with kidney
yang
deficiency syndrome as an example, and to provide methodological reference for the standardization of syndrome differentiation.
Methods
2
High-frequency clinical features of POP were screened by descriptive statistical analysis, and strong association features of POP were obtained by association rule algorithm. On this basis, a latent structure (latent tree) model was established through latent structure analysis, and the implicit and explicit variables (features) related to POP with kidney
yang
deficiency syndrome were comprehensively clustered, and the clustering results were interpreted by the indexes of mutual information and cumulative information coverage, to explore the primary and secondary symptoms, and to deduce the categories of POP with kidney
yang
deficiency syndrome based on the probability of the features appearing in the various latent categories. Based on the categories, the clinical feature scores and identification thresholds were calculated, and the syndrome differentiation model of POP with kidney
yang
deficiency was initially constructed by combining the comprehensive judgment rules. Finally, the results of TCM professionals' judgment were used as the gold standard to further evaluate the effectiveness of the model in assisting the syndrome differentiation.
Results
2
The 32 features strongly associated with POP were obtained, and the Bayes information critedon score of the further constructed latent tree model was -15291.93. Based on the mutual information and the cumulative information coverage, the main symptoms of POP with kidney
yang
deficiency syndrome were bone weakness, fatigue, pale tongue, clear urine, frequent nocturnal urination, cold limbs, thin pulse, white coating, and secondary symptoms were weakness, loss of libido, loose stool, frequent urination, lumbar and knee weakness, and fear of cold. From the probability of the occurrence of each clinical feature in different latent categories of POP with kidney
yang
deficiency syndrome, the state was introduced as S0 category (none/mild kidney
yang
syndrome)/ S1 category (moderate kidney
yang
syndrome)/ S2 category (severe kidney
yang
syndrome). Optimizing the preliminary rules of state identification and refining the state of S1 category, the results showed that among 970 patients with POP, there were 520 patients having no/mild kidney
yang
deficiency syndrome, 224 patients with moderate to mild kidney
yang
deficiency syndrome, 81 patients with moderate to severe kidney
yang
deficiency syndrome, and 145 patients with severe kidney
yang
deficiency syndrome. During the evaluation and validation process, the correct rate of the model assessment index was 0.8835, while the sensitivity was 0.7181, and the specificity was 0.9437.
Conclusion
2
Combined with the latent structure analysis of the association rule, the syndrome differentiation model for POP with kidney
yang
deficiency could be constructed, and the model shows a good quantitative identification effect, which can provide methodological supports for clinicians to improve the efficiency and accuracy of TCM diagnosis.
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