What Can We Learn About Drug Safety and Other Effects in the Era of Electronic Health Records and Big Data That We Would Not Be Able to Learn From Classic Epidemiology?
What Can We Learn About Drug Safety and Other Effects in the Era of Electronic Health Records and Big Data That We Would Not Be Able to Learn From Classic Epidemiology?
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关于电子健康记录和大数据时代的药物安全性和其他影响，我们能学到哪些经典流行病学无法学到的知识？
DOI：
10.1016/j.jss.2019.09.053
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Huo,Zhiguang
中科院分区：
文献类型：
--
作者：
Zarrinpar,Ali;DavidCheng,Ting-Yuan;Huo,Zhiguang
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As more and more health systems have converted to the use of electronic health records, the amount of searchable and analyzable data is exploding. This includes not just provider or laboratory created data but also data collected by instruments, personal devices, and patients themselves, among others. This has led to more attention being paid to the analysis of these data to answer previously unaddressed questions. This is especially important given the number of therapies previously found to be beneficial in clinical trials that are currently being re-scrutinized. Because there are orders of magnitude more information contained in these data sets, a fundamentally different approach needs to be taken to their processing and analysis and the generation of knowledge. Health care and medicine are drivers of this phenomenon and will ultimately be the main beneficiaries. Concurrently, many different types of questions can now be asked using these data sets. Research groups have become increasingly active in mining large data sets, including nationwide health care databases, to learn about associations of medication use and various unrelated diseases such as cancer. Given the recent increase in research activity in this area, its promise to radically change clinical research, and the relative lack of widespread knowledge about its potential and advances, we surveyed the available literature to understand the strengths and limitations of these new tools. We also outline new databases and techniques that are available to researchers worldwide, with special focus on work pertaining to the broad and rapid monitoring of drug safety and secondary effects.
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影响因子：
3.9
作者：
Bruun SB;Petersen I;Kristensen NR;Cronin-Fenton D;Pedersen AB
通讯作者：
Pedersen AB
生物样本、研究同意书和区分细胞系研究。
影响因子：
28.4
作者：
Spector-Bagdady,Kayte;FernandezLynch,Holly;Brenner,JChad;Shuman,AndrewG
通讯作者：
Shuman,AndrewG
DOI：
10.1136/bmj.h1798
发表时间：
2015-04-17
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Furu K;Kieler H;Haglund B;Engeland A;Selmer R;Stephansson O;Valdimarsdottir UA;Zoega H;Artama M;Gissler M;Malm H;Nørgaard M
通讯作者：
Nørgaard M
影响因子：
3.3
作者：
Toh S
通讯作者：
Toh S
使用医疗记录中的时间模式从适应症中辨别药物不良事件。
DOI：
--
发表时间：
2012
期刊：
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
影响因子：
--
作者：
Liu,Yi;Lependu,Paea;Iyer,Srinivasan;Shah,NigamH
通讯作者：
Shah,NigamH