Department of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 14115-143, Iran.
Department of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 14115-143, Iran.
Int J Med Inform. 2020 Aug;140:104161. doi: 10.1016/j.ijmedinf.2020.104161. Epub 2020 May 4.
There is an increasing trend in using network science methods and algorithms, including community detection methods, in different areas of healthcare. These areas include protein networks, drug prescriptions, healthcare fraud detection, and drug abuse. Counterfeit drugs, off-label marketing issues, and finding the healthcare community structures in a network of hospitals, are examples of using community detection in healthcare.
This paper attempts to find physicians' real medical specialties based on their prescription history. As a novel application of community detection in the healthcare field, this knowledge can be used as an alternative for missing values of the healthcare databases. Therefore, it could help scientists and researchers to obtain more accurate and more reliable results.
This research is done through the community detection method and applying big data tools as well as interviews with the field experts. The big data, which is used in this paper, includes 32 million written medical prescriptions in the year 2014, provided by the Health Insurance Organization. The results are validated both qualitatively and quantitatively.
The findings reveal nine major communities of physicians, and labeling these communities by experts presents almost every specialty in the drug prescriptions field. Some of these communities are labeled as a single well-known specialty, and some others are consist of two or more specialties that have overlap with each other.
After receiving the prescription data and getting the experts' opinions, it was revealed that some physicians might persistently prescribe drugs that are not in their fields of expertise. Regarding the accuracy of community detection and the use of existing data values, we proved this hypothesis.
在医疗保健的不同领域，包括蛋白质网络、药物处方、医疗保健欺诈检测和药物滥用，使用网络科学方法和算法（包括社区检测方法）的趋势正在增加。假药、标签外营销问题以及在医院网络中寻找医疗保健社区结构，都是在医疗保健中使用社区检测的例子。
本文试图根据医生的处方记录找到他们的真实医学专业。作为社区检测在医疗保健领域的新应用，这种知识可以用作医疗保健数据库中缺失值的替代。因此，它可以帮助科学家和研究人员获得更准确和更可靠的结果。
这项研究是通过社区检测方法和应用大数据工具以及与领域专家的访谈来完成的。本文使用的大数据包括健康保险组织提供的 2014 年 3200 万份书面处方。结果通过定性和定量进行验证。
研究结果揭示了 9 个主要的医生群体，专家对这些群体进行标注，几乎涵盖了药物处方领域的每一个专业。其中一些群体被标注为一个单一的知名专业，而另一些群体则由相互重叠的两个或多个专业组成。
在收到处方数据并获得专家意见后，研究发现一些医生可能会持续开他们专业领域之外的药物。关于社区检测的准确性和现有数据值的使用，我们验证了这一假设。
