School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China.
Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
Front Med. 2018 Apr;12(2):206-217. doi: 10.1007/s11684-017-0525-8. Epub 2017 Jun 14.
Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb-symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.
确定针对特定疾病状况或特定患者群体的有效中药（TCM）治疗方法是一个困难的问题，由于真实患者表现出复杂的个性化特征以及临床环境中规定的个体化组合疗法，因此需要进行调查。在这项研究中，提出了一种多阶段分析方法，该方法集成了倾向病例匹配、复杂网络分析和草药集富集分析，以确定针对特定疾病（例如失眠）的有效草药处方。首先，应用倾向病例匹配来匹配临床病例。然后，结合核心网络提取和草药集富集来检测核心有效草药处方。基于有效性的互信息用于检测强草药-症状关系。该方法应用于从精心设计的观察性研究中收集的 955 例患者的中医临床数据集。结果表明，与原始处方相比，发现了具有更高有效率的草药处方组（匹配样本为 76.9%比 42.8%；所有样本为 94.2%比 84.9%）。还确定了具有特定症状表现的特定患者群体，以帮助研究有效草药处方的适应症。
