Satwika Mandali V, Sushma Dudala S, Jaiswal Varun, Asha Syed, Pal Tarun
Department of Biotechnology, Vignan's Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur, 522213, Andhra Pradesh, India.
School of Electrical and Computer Science Engineering, Shoolini University, Solan, Himachal Pradesh, 173212, India.
Recent Pat Biotechnol. 2021;15(1):34-50. doi: 10.2174/1872208314666201021162704.
The immediate automatic systemic monitoring and reporting of adverse drug reactions, improving the efficacy is the utmost need of the medical informatics community. The venturing of advanced digital technologies into the health sector has opened new avenues for rapid monitoring. In recent years, data shared through social media, mobile apps, and other social websites has increased manifolds requiring data mining techniques.
The objective of this report is to highlight the role of advanced technologies together with the traditional methods to proactively aid in the early detection of adverse drug reactions concerned with drug safety and pharmacovigilance.
A thorough search was conducted on papers and patents regarding pharmacovigilance. All articles with respect to the relevant subject were explored and mined from public repositories such as Pubmed, Google Scholar, Springer, ScienceDirect (Elsevier), Web of Science, etc. Results: The European Union's Innovative Medicines Initiative WEB-RADR project has emphasized the development of mobile applications and social media data for reporting adverse effects. Only relevant data has to be captured through the data mining algorithms (DMAs) as it plays an important role in timely prediction of risk with high accuracy using two popular approaches; the frequentist and Bayesian approach. Pharmacovigilance at the pre-marketing stage is useful for the prediction of adverse drug reactions in the early developmental stage of a drug. Later, post-marketing safety reports and clinical data reports are important to be monitored through electronic health records, prescription-event monitoring, spontaneous reporting databases, etc. Conclusion: The advanced technologies supplemented with traditional technologies are the need of the hour for evaluating a product's risk profile and reducing risk in population especially with comorbid conditions and on concomitant medications.
对药物不良反应进行即时自动的系统监测和报告，提高疗效是医学信息学界的迫切需求。先进数字技术涉足卫生领域为快速监测开辟了新途径。近年来，通过社交媒体、移动应用程序及其他社交网站共享的数据呈数倍增长，这需要数据挖掘技术。
本报告的目的是强调先进技术与传统方法在积极协助早期发现与药物安全和药物警戒相关的药物不良反应方面的作用。
对有关药物警戒警戒的论文和专利进行全面检索。从诸如PubMed、谷歌学术、施普林格、科学Direct（爱思唯尔）、科学网等公共数据库中搜索并挖掘所有相关主题的文章。结果：欧盟的创新药物计划WEB-RADR项目强调了开发用于报告不良反应的移动应用程序和社交媒体数据。只需通过数据挖掘算法（DMA）捕获相关数据，因为它在使用两种常用方法（频率论方法和贝叶斯方法）以高精度及时预测风险方面发挥着重要作用。上市前阶段的药物警戒有助于在药物早期研发阶段预测药物不良反应。之后，上市后安全报告和临床数据报告通过电子健康记录、处方事件监测、自发报告数据库等进行监测很重要。结论：先进技术辅以传统技术是评估产品风险状况和降低人群尤其是患有合并症和正在使用伴随药物人群风险的当务之急。
