Building a disease risk model of osteoporosis based on traditional Chinese medicine symptoms and western medicine risk factors
Building a disease risk model of osteoporosis based on traditional Chinese medicine symptoms and western medicine risk factors
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DOI：
10.1002/sim.4382
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发表时间：
2012-03
影响因子：
2
通讯作者：
Xiao-Hua Zhou;Xiao-Hua Zhou;S. L. Li;F. Tian;B. Cai;Y. M. Xie;Y. Pei;S. Kang;M. Fan;J. P. Li
中科院分区：
文献类型：
--
作者：
Xiao-Hua Zhou;Xiao-Hua Zhou;S. L. Li;F. Tian;B. Cai;Y. M. Xie;Y. Pei;S. Kang;M. Fan;J. P. Li
在我们团队进行的中医药横断面调查中，我们对确定骨质疏松症的危险因素感兴趣。为了分析这项中医研究，我们必须处理三个统计问题：(1)非常大量的潜在风险因素，(2)潜在风险因素之间的相互作用，(3)一些连续尺度风险因素的非线性效应。为了解决这些分析问题，我们使用了支持向量机递归特征消除和随机森林两种数据挖掘方法；为了处理高维风险因素的诅咒，我们应用了另一种数据挖掘技术--关联规则学习来发现风险因素之间的潜在关联。最后，我们使用广义部分线性模型(GPLM)来确定一个重要的连续尺度风险因素的非线性效应。最终的GPLM模型表明，中医症状在评估骨质疏松风险中发挥着重要作用。GPLM还揭示了重要风险因素--绝经年限的非线性影响，而广义线性模型可能忽略了这一点。版权所有©2012 John Wiley&Sons，Ltd.
In the Traditional Chinese Medicine (TCM) cross‐sectional survey conducted by our team, we were interested in determining the risk factors of osteoporosis. To analyze this TCM study, we had to deal with three statistical problems: (1) a very large number of potential risk factors, (2) interactions among potential risk factors, and (3) nonlinear effects of some continuous‐scale risk factors. To address these analytic issues, we used two data mining methods, support vector machine recursive feature elimination and random forest; to deal with the curse of high‐dimensional risk factors, we applied another data mining technique of association rule learning to discover the potential associations among risk factors. Finally, we employed the generalized partial linear model (GPLM) to determine nonlinear effects of an important continuous‐scale risk factor. The final GPLM model shows that TCM symptoms play an important role in assessing the risk of osteoporosis. The GPLM also reveals a nonlinear effect of the important risk factor, menopause years, which might be missed by the generalized linear model. Copyright © 2012 John Wiley & Sons, Ltd.