Study on Main Drugs and Drug Combinations of Patient-Controlled Analgesia Based on Text Mining
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, t...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Pain Research and Management |
| Online Access: | http://dx.doi.org/10.1155/2020/8517652 |
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| author | Xing Jin Ying Wu |
| author_facet | Xing Jin Ying Wu |
| author_sort | Xing Jin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-572a6c6192534f549f42c5c87068123c |
| institution | Kabale University |
| issn | 1203-6765 1918-1523 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Pain Research and Management |
| spelling | doaj-art-572a6c6192534f549f42c5c87068123c2025-02-03T06:45:59ZengWileyPain Research and Management1203-67651918-15232020-01-01202010.1155/2020/85176528517652Study on Main Drugs and Drug Combinations of Patient-Controlled Analgesia Based on Text MiningXing Jin0Ying Wu1Department of Anesthesiology, Shanxi Cancer Hospital, Taiyuan 030013, ChinaSchool of Humanities and Social Sciences, Shanxi Medical University, Taiyuan 030001, ChinaIn 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.http://dx.doi.org/10.1155/2020/8517652 |
| spellingShingle | Xing Jin Ying Wu Study on Main Drugs and Drug Combinations of Patient-Controlled Analgesia Based on Text Mining Pain Research and Management |
| title | Study on Main Drugs and Drug Combinations of Patient-Controlled Analgesia Based on Text Mining |
| title_full | Study on Main Drugs and Drug Combinations of Patient-Controlled Analgesia Based on Text Mining |
| title_fullStr | Study on Main Drugs and Drug Combinations of Patient-Controlled Analgesia Based on Text Mining |
| title_full_unstemmed | Study on Main Drugs and Drug Combinations of Patient-Controlled Analgesia Based on Text Mining |
| title_short | Study on Main Drugs and Drug Combinations of Patient-Controlled Analgesia Based on Text Mining |
| title_sort | study on main drugs and drug combinations of patient controlled analgesia based on text mining |
| url | http://dx.doi.org/10.1155/2020/8517652 |
| work_keys_str_mv | AT xingjin studyonmaindrugsanddrugcombinationsofpatientcontrolledanalgesiabasedontextmining AT yingwu studyonmaindrugsanddrugcombinationsofpatientcontrolledanalgesiabasedontextmining |