Duan Rui, Zhang Xinyuan, Du Jingcheng, Huang Jing, Tao Cui, Chen Yong
University of Pennsylvania, Philadelphia, PA.
University of Texas Health Science Center at Houston, Houston, TX.
Data Min Big Data (2017). 2017;2017:379-389. Epub 2017 Jun 24.
Healthcare is going through a big data revolution. The amount of data generated by healthcare is expected to increase significantly in the coming years. Therefore, efficient and effective data processing methods are required to transform data into information. In addition, applying statistical analysis can transform the information into useful knowledge. We developed a data mining method that can uncover new knowledge in this enormous field for clinical decision making while generating scientific methods and hypotheses. The proposed pipeline can be generally applied to a variety of data mining tasks in medical informatics. For this study, we applied the proposed pipeline for post-marketing surveillance on drug safety using FAERS, the data warehouse created by FDA. We used 14 kinds of neurology drugs to illustrate our methods. Our result indicated that this approach can successfully reveal insight for further drug safety evaluation.
医疗保健正在经历一场大数据革命。预计未来几年医疗保健产生的数据量将大幅增加。因此,需要高效且有效的数据处理方法将数据转化为信息。此外,应用统计分析可以将信息转化为有用的知识。我们开发了一种数据挖掘方法,该方法能够在这个庞大的领域中发现新知识以用于临床决策,同时生成科学方法和假设。所提出的流程通常可应用于医学信息学中的各种数据挖掘任务。在本研究中,我们将所提出的流程应用于使用FDA创建的数据仓库FAERS进行药品安全性的上市后监测。我们使用了14种神经学药物来说明我们的方法。我们的结果表明,这种方法能够成功地揭示见解以用于进一步的药物安全性评估。