Application of Modern Computer Technology in Adverse Drug Reactions
DOI: 10.23977/acss.2019.31005 | Downloads: 13 | Views: 1146
Qiwei Liu 1, Yongsai Yan 1
1 School of Software, Jiangxi Normal University, Nanchang, 330000, China
Corresponding AuthorQiwei Liu
To explore the application of data mining technology in adverse drug reaction (ADR), and provide reference for exploring new methods in the field of ADR monitoring in China. Searching for database related documents such as China Knowledge Network and Data with keywords such as “data mining”, “adverse drug reaction”, “electronic medical record” and “hospital information system”, data mining in spontaneous reporting system and electronic medical treatment the current status, common methods, advantages and disadvantages of ADR monitoring are reviewed. Data mining technology can effectively detect ADR signals in both spontaneous reporting systems and electronic medical records. It has excellent data analysis and ability to discover patterns and will play an important role in the field of ADR monitoring.
KEYWORDSdata mining, adverse drug reactions, spontaneous reporting system, detection method
CITE THIS PAPER
Qiwei Liu, Yongsai Yan, Application of Modern Computer Technology in Adverse Drug Reactions. Advances in Computer, Signals and Systems (2019) Vol. 3: 25-30. DOI: http://dx.doi.org/10.23977/acss.2019.31005.
 Dumouchel W.Bayesian data mining in large frequency table with an application to FDA spontaneous reporting system[J]. Am Stat,1999,53(3):177-190.
 DuMouchel W,Smith ET,Beasley R,et al,Association of asthma therapy and Churg-Strauss syndrome:an analysis of postmarketing surveillance data[J].Clin Ther,2004,26(7):1092-1104.
 Bayesian neural networks with confidence estimations applied to data mining[J]. R Orre,A Lansner,A Bate,M Lindquist. Computational Statistics and Data Analysis. 2000(4).
 A Data Mining Approach for SignalDetection and Analysis[J]Volume 55, September 2014, Pages 343-352.
 Bayesian confidence propagation neural network. [J] 2007.30.7.623-625.
 Application of data mining technology in the field of adverse drug reaction monitoring [j]. Zhang Wei, Yu Jia, Lu Liang, et al. Chinese Pharmacy, 2014.
 CHEN Wenge, LI Yijuan, JIANG Jing, et al. Research on signal detection and automatic early warning technology for adverse drug reactions based on BCPNN method[J].Application Research of Computers,2009,26(4).
 Research progress on data mining of adverse drug events [j]. Wang Liwei, Yu Yue, Wang Wei. Chinese Journal of Pharmacy. 2013.10.10.
 Social media-based emergency information mining and analysis[J]. Wang Yandong, Li Wei, Wang Teng, Zhu Jianqi. Journal of Wuhan University (Information Science Edition). 2016(03)
 National Annual Report on Adverse Drug Reaction Monitoring (2015) [J]. Middle-aged and elderly health care. 2016(09)
 Overview of research and application of data mining technology in Chinese medicine field [J]. Wang Qian, Sheng Hui, Jin Wei. Hunan Journal of Traditional Chinese Medicine. 2015(03)
 Advances in the application of data mining technology in the field of traditional Chinese medicine research [J]. Cui Liang; Lu Ping. Xinjiang Traditional Chinese Medicine. 2017.6.25
 Research on Knowledge Integration and Utilization of Adverse Drug Reactions Based on Big Data Mining[J].Yu Yue.Jilin University.2016.3.1
 Kang Qi, Liang Ping, Song Minxian. Definition of “normal” in the definition of adverse drug reactions[J]. Chinese Journal of Traditional Medicine and Pharmacy, 2013, 4(1): 50.
 Liu Jing. Analysis of the incidence of adverse drug reactions in anti-tuberculosis[J]. Chinese Medicine Guide, 2014, 12(9): 73.
 Lei Jianping. Current status and progress of prevention and treatment of adverse reactions of TB chemotherapy drugs in China[J]. China National Defense Magazine, 2014, 36(9): 774.
 Yu Chao, Xu Yuxi, Li Xinling, et al. Hospital information system and national drug abuse in China
 Investigation on the demand for docking of reaction monitoring system[J]. Chinese Medical Journal, 2016, 13(3): 154-158.
 Wang Ling. Research on Adverse Drug Reaction Monitoring Based on Hospital Information System[J]. Chinese Medical Journal, 2015, 12(4): 229-231.