Department of Anesthesiology, Shanxi Cancer Hospital, Taiyuan 030013, China.
School of Humanities and Social Sciences, Shanxi Medical University, Taiyuan 030001, China.
Pain Res Manag. 2020 May 23;2020:8517652. doi: 10.1155/2020/8517652. eCollection 2020.
In recent years, with the continuous understanding of pain knowledge and the continuous improvement of quality of life requirements, patient-controlled analgesia (PCA) has been widely used in a variety of pain patients. In this study, text mining technology was used to analyze relevant literature, try to find out the main drugs of PCA, classify the drugs, and dig out the important drug combination rules. PCA studies were retrieved from PubMed database in recent 10 years, and the bibliographic information of the literatures was taken as mining sample. First, the names of the drugs in the sample were identified by MetaMap package; then, Bicomb software was used to extract high-frequency drugs for the word frequency analysis and to construct a drug-sentence matrix. Finally, "hclust" package and "arules" package of R were used for the cluster analysis and association analysis of drugs. 39 main PCA drugs were screened out. Morphine, dexmedetomidine, and fentanyl were the top three drugs. Through cluster analysis, these drugs were divided into two clusters, one containing 26 common drugs and the other containing 13 core drugs. The association analysis of these drugs was carried out, and 22 frequent itemsets and 6 association rules were obtained. The maximum frequent 1-itemset was {Morphine} and the maximum frequent 2-itemset was {Morphine, Ropivacaine}. The research results have certain guidance and reference value for clinicians and researchers. In addition, it provides a way to study the relationship between drugs from the perspective of text mining.
近年来，随着人们对疼痛知识的不断深入了解和对生活质量要求的不断提高，患者自控镇痛（PCA）已广泛应用于各种疼痛患者。本研究采用文本挖掘技术对相关文献进行分析，试图找出 PCA 的主要药物，对药物进行分类，并挖掘出重要的药物组合规则。从 PubMed 数据库中检索近 10 年来的 PCA 研究文献，以文献的书目信息作为挖掘样本。首先，使用 MetaMap 软件包识别样本中药物的名称；然后，使用 Bicomb 软件提取高频药物进行词频分析，并构建药物-句子矩阵。最后，使用 R 中的“hclust”包和“arules”包对药物进行聚类分析和关联分析。筛选出 39 种主要的 PCA 药物。吗啡、右美托咪定和芬太尼是排名前三的药物。通过聚类分析，这些药物分为两簇，一簇包含 26 种常用药物，另一簇包含 13 种核心药物。对这些药物进行关联分析，得到 22 个频繁项集和 6 条关联规则。最大频繁 1 项集为{Morphine}，最大频繁 2 项集为{Morphine, Ropivacaine}。研究结果对临床医生和研究人员具有一定的指导和参考价值。此外，它还提供了一种从文本挖掘的角度研究药物之间关系的方法。
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