Study on the main drugs and drug combinations of patient-controlled analgesia based on text mining

Background: 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 the treatment of PCA, more and more patients use multimodal analgesia (MMA). In this study, text mining technology was used to analyze relevant literatures, try to find out the main drugs of PCA, classify the drugs, and dig out the important drug combination rules. Methods PCA literatures were retrieved from Pubmed database in recent 10 years, and the bibliographic information of the literatures was taken as mining samples. First, the names of the drugs in the sample is identified by MetaMap package, then Bicomb software was used to extract high-frequency drugs for word frequency analysis and to construct a drug-phrase matrix. Finally, ‘hclust’ package and ‘arules’ package of R were used for cluster analysis and association analysis of drugs. Results 39 main PCA drugs were screened out, Morphine, Dexmedetomidine and Fentanyl were the top three drugs. Through cluster analysis, these drugs are 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-itemsets were {Morphine} and the maximum frequent 2-itemsets were {Morphine, Ropivacaine}. Conclusions 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.