Institute for Medical Biometry and Informatics, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany.
School of Translational Information Technologies, ITMO University, Kronverksky Pr. 49, 197101, Saint-Petersburg, Russia.
J Med Syst. 2020 Mar 12;44(4):86. doi: 10.1007/s10916-020-1538-4.
Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis.
图论是一种成熟的理论，在数学中使用了许多方法来研究图结构。在医学领域，电子健康记录（EHR）常用于存储和分析患者数据。因此，将 EHR 数据建模为图似乎是很直接的。本系统文献综述旨在调查当前代表和处理患者数据的图研究领域的前沿。我们希望展示在这种情况下需要进一步研究的研究领域。使用搜索词“健康记录、图和相关术语”，在 MEDLINE、Web of Science、IEEE Xplore 和 ACM 数字图书馆数据库中查询。根据“系统评价和荟萃分析的首选报告项目”（PRISMA）声明指南，通过全文分析筛选和评估文章。在系统文献综述中发现的 383 篇文章中，最终有 11 篇被纳入本文献综述进行分析。它们中的大多数使用图来表示时间关系，通常表示实验室数据点之间的连接。只有两篇论文报告说使用相似性测量来比较患者图，从而进一步处理图数据。代表个体患者的图在研究中很少使用，只有 11 篇论文在其研究中考虑了这种图。已经成熟的图论算法的潜力可以帮助该研究领域的发展，但目前论文太少，无法估计该研究领域将如何发展。总之，使用这种患者图可以成为一种很有前途的技术，用于使用相似性测量或不同类型的分析来开发诊断、用药或治疗患者的决策支持系统。
