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• W3139519150 abstract "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." @default.
• W3139519150 created "2021-03-29" @default.
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• W3139519150 date "2020-11-01" @default.
• W3139519150 modified "2023-09-26" @default.
• W3139519150 title "Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter" @default.
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• W3139519150 doi "https://doi.org/10.1109/ichi48887.2020.9374390" @default.
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