摘要
中风病病因病机十分复杂,导致了中风病的证候繁杂多变。中风患者发病后病情一直处于动态变化中,把握中风证候的动态演变规律,对于临床果断采取准确的治疗方法及处方用药具有重要的意义。证候是由许多因素组成的复杂系统,包括发病时间、性别、地域、基础疾病和体质因素等,难以用单一的生理、生化指标来表达。系统生物学整合了数据驱动和模型驱动的研究策略,融合了计算与实验的研究手段,从而获得系统与整体相关的规律性认识。在中风的证候学研究中,多采用以数据驱动的研究方法,而模型驱动的方法包括人工神经网络、贝叶斯网络等可为中风证候动态研究提供新的研究思路。本文综述了用数据驱动模式对中风证候动态变化规律的研究,并提出了中风证候的模型驱动研究方向。
Many clinical studies showed that the traditional Chinese medicine （TCM） syndromes in stroke have been dynamically changing since the onset of the disease. The changing of TCM syndromes can be attributed to multiple correlative factors such as age, sex, area distribution, underlying diseases, and constitutional factor. Data-driven methods involving multivariate statistical methods and descriptive approach have been used to analyze the regularity of dynamically changed TCM syndromes of stroke. However, expressing non- linear relationship between symptom or correlative factors and syndrome patterns by data-driven models is challenging. Model-driven methods involving artificial neural networks and Bayesian networks are new methods for studying the changes in TCM syndromes in patients with stroke. In this review, the authors summarized the studies of dynamically changed patterns of stroke syndromes based on data-driven methods and some clinical trials on TCM syndromes based on model-driven methods. Further studies are needed to improve the understanding of the dynamically changing regularity of TCM syndromes for stroke by using model- driven methods so as to develop appropriate and timely TCM treatments.
出处
《中西医结合学报》
CAS
2011年第12期1292-1300,共9页
Journal of Chinese Integrative Medicine
基金
上海市卫生局重点项目
国家中医药管理局重点学科项目
公益性行业科研专项资金项目(No.201007002)
关键词
中风
证候
数据驱动
模型驱动
人工神经网络
stroke
syndrome
data-driven
model-driven
artificial neural networks
