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Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter

机译：高阶网络的糖尿病合并症：重要的疾病轨迹

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摘要

Networks are powerful and flexible structures for modeling relationships in medical and biological systems, but in a traditional first-order network representation, an edge typically expresses a relationship between a single pair of nodes. In order to analyze complex relationships between groups of nodes, researchers rely on combined sets of these pairwise connections, which can misrepresent the true relationships in the underlying data. Higher-order networks, on the other hand, capture the higher-order dependencies that go beyond the pairwise interactions, and thus can encode more complex relationships within a familiar structure. In this study, we created and analyzed higher-order networks of disease trajectories generated from the records of 913,475 type 2 diabetes patients. We show that higher-order networks provide a more accurate representation of the underlying disease trajectories than traditional first-order networks. We also analyze differences in PageRank scores and community structure at higher orders and discuss the implications of these differences for the future study of comorbidity networks.
机译：网络是强大且灵活的结构，用于建模医疗和生物系统中的关系，但在传统的一阶网络表示中，边缘通常表示单个节点之间的关系。为了分析节点组之间的复杂关系，研究人员依赖于这些成对连接的组合集，这可以歪曲基础数据中的真正关系。另一方面，高阶网络捕获超出相互作用的高阶依赖项，因此可以在熟悉的结构内编码更复杂的关系。在这项研究中，我们创建并分析了从913,475型糖尿病患者的记录产生的高阶网络的疾病轨迹。我们表明高阶网络提供了比传统的一阶网络的潜在疾病轨迹更准确的表示。我们还在更高的订单中分析了PageRank分数和社区结构的差异，并讨论了这些差异对合并网络的未来研究的影响。

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