Multimorbidity, the presence of two or more chronic conditions within an individual, represents one of the most intricate challenges for global health systems. Traditional single-disease management often fails to address the multifaceted nature of multimorbidity. Network model emerges as a growing field for elucidating the interconnections among multimorbidity. However, the field lacks a standardized method to compute and visually represent of these networks. Given the challenges, this study proposes a three-stage methodology to decipher multimorbidity. First, we integrate the Failure Modes and Effects Analysis (FMEA) method with the multimorbidity encapsulation framework to develop the Multimorbidity Risk Network (MRN). Second, we use complex network techniques to identify high-risk patterns within MRN communities. Finally, we apply machine learning techniques to correlate these communities with the biological attributes of patients that have been marginalized in most studies. Our approach advocates a paradigm shift from the conventional focus on single diseases to a holistic, patient-centric approach, providing decision-makers with integrated information technology artifacts for deciphering the multimorbidity.

中文翻译：

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我们可以从多发病中学到什么？深入探讨其风险模式到相应的患者概况


多重发病，即一个人体内存在两种或多种慢性病，是全球卫生系统面临的最复杂的挑战之一。传统的单一疾病管理往往无法解决多发病的多方面性质。网络模型成为阐明多种疾病之间相互联系的一个不断发展的领域。然而，该领域缺乏计算和直观地表示这些网络的标准化方法。考虑到这些挑战，本研究提出了一种三阶段方法来解读多发病。首先，我们将故障模式和影响分析（FMEA）方法与多病封装框架相结合，开发多病风险网络（MRN）。其次，我们使用复杂的网络技术来识别 MRN 社区内的高风险模式。最后，我们应用机器学习技术将这些社区与在大多数研究中被边缘化的患者的生物学属性相关联。我们的方法倡导从传统的单一疾病关注转向以患者为中心的整体方法的范式转变，为决策者提供用于破译多发病的综合信息技术工件。