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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-07-01
摘要
疾病 interaction in multimorbid patients is relevant to treatment and prognosis, yet poorly understood. In the present work, we combine approaches from 網絡 science, 機器學習 and 計算 phenotyping to assess interactions between two or more 疾病s in a transparent way across the full diagnostic spectrum. We demonstrate that health states of hospitalized patients can be better characterized by including higher-order features capturing interactions between more than two 疾病s. We identify a meaningful set of higher-order diagnosis features that account for synergistic 疾病 interactions in a 族群-wide (N = 9 M) medical claims dataset. We construct a generalized 疾病 網絡 where (higher- order) diagnosis features are linked if they predict similar diagnoses across the whole diagnostic spectrum. The fact that specific d
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