Bioprocess Engineering, Otto von Guericke University, Universitätsplatz 2, Magdeburg 39106, Germany.
Database and Software Engineering Group, Otto von Guericke University, Universitätsplatz 2, Magdeburg 39106, Germany.
Database (Oxford). 2023 Jul 10;2023. doi: 10.1093/database/baad045.
The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms. Graphical abstract.
临床数据的数量和复杂性不断增加，这就需要一种合适的方法来存储和分析这些数据。传统的方法使用表格结构（关系型数据库）来存储数据，从而使存储和检索临床领域的相关数据变得复杂。图形数据库通过将数据存储在图形中作为节点（顶点），并用边（链接）连接这些节点，为解决这个问题提供了很好的解决方案。底层的图形结构可用于后续的数据分析（图形学习）。图形学习由两部分组成：图形表示学习和图形分析。图形表示学习旨在将高维输入图形降低到低维表示。然后，图形分析使用获得的表示形式来执行可视化、分类、链路预测和聚类等分析任务，这些任务可用于解决特定于领域的问题。在本次调查中，我们回顾了当前最先进的图形数据库管理系统、图形学习算法以及临床领域的各种图形应用程序。此外，我们提供了一个全面的用例，以更清楚地了解复杂的图形学习算法。
