Analysis of Research Trends in Drug Informatics Based on Articles in the Japanese Journal of Drug Informatics
10.11256/jjdi.14.26
- VernacularTitle:医薬品情報学掲載論文からみた医薬品情報学に関する研究動向の解析
- Author:
Fusao Komada
;
Yuko Nakayama
;
Atsushi Kinoshita
- Publication Type:Journal Article
- Keywords:
key word;
abstract;
co-occurrence network;
research trend;
Japanese Journal of Drug Informatics
- From:Japanese Journal of Drug Informatics
2012;14(1):26-34
- CountryJapan
- Language:English
-
Abstract:
Objective: We analyzed articles in the Japanese Journal of Drug Informatics with the goal of identifying recent research trends in drug informatics.
Method: The appearance frequencies of keywords in the Japanese Journal of Drug Informatics (2001: vol. 3 (1) to 2009: vol. 11 (4)) and Japanese Journal of Pharmaceutical Health Care and Sciences (2009: vol. 35 (1) to (6)), and words in abstracts in Japanese Journal of Drug Informatics (2009: vol. 11 (1) to 2010: vol. 12 (4)) were analyzed.
Results: To investigate keywords in the Japanese Journal of Drug Informatics, appearance frequencies of information, drug, drugs and pharmacist in 2001: vol. 3 (1) to 2003: vol. 5 (4), those of information, drug, drugs, medical, medication and questionnaire in 2004: vol. 6 (1) to 2006: vol. 8 (4), and those of information, drug, questionnaire, survey, pharmacist, adverse and generic in 2007: vol. 9 (1) to 2009: vol. 11 (4) were higher than those of other keywords. In the Japanese Journal of Pharmaceutical Health Care and Sciences, appearance frequencies of drug, pharmacy, care, patient, pharmaceutical, cancer, education, training, analysis and drugs were higher than those of other keywords. Information, drug(s), patients, pharmacists, hospital, use, questionnaire, medical, adverse, survey, agents, generic and pharmaceutical were high frequency words used in abstracts published in the Japanese Journal of Drug Informatics. These words in abstracts indicate a Zipf’s law-like rank distribution. Co-occurrence network graphs using abstracts showed that the first cluster consisted of medical, drug, adverse, drugs, pharmaceutical, hospital, doctors, contents and drug around information and pharmacists as hubs, and the second cluster consisted of 3 words (agents, woman and pregnant). Furthermore, co-occurrence network graphs indicated that care, medical, pharmaceutical, information, adverse, pharmacists, hospital, doctors, questionnaire, woman, pregnant, package and side were matters of important arguments and/or phenomena.
Conclusion: These data suggest that the scope of themes in articles published in the Japanese Journal of Drug Informatics is establishing definitive categories. The recent themes and contents of the Japanese Journal of Drug Informatics were closely and mutually related.
