Identifying relationships between components of an index helps to have a better understanding of the condition they define. The Frailty Index (FI) measures the global health of individuals and can be used to predict outcomes as mortality. Previously, we modelled the relationship between the FI components (deficits) and death through an undirected graphical model, analyzing their relevance from a social network analysis framework. Here, we model the FI components and death through an averaged Bayesian network obtained through a structural learning process and resampling, to understand how the FI components and death are causally related. We identified that the components are not similarly related between them and that deficits are related according to its type. Two deficits were the most relevant in terms of their connections and other two were directly associated with death. We also obtained the strength of the relationships to identify the most plausible, identifying clusters of deficits heavily related. Finally, we propagated evidence (assigned values to all deficits) and studied how FI components predict mortality, obtaining a correct assignation of almost 74%, whereas a sensitivity of 56%. As a classifier of death, the more number of deficits included for the evidence, the best performance; but the FI seems not to be very good to correctly classify death people.

中文翻译：

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了解脆弱性：通过贝叶斯网络和证据传播，组件之间的概率因果关系及其与死亡的关系

识别索引组件之间的关系有助于更好地理解它们定义的条件。衰弱指数 (FI) 衡量个人的全球健康状况，可用于预测死亡率的结果。以前，我们通过无向图模型对 FI 组件（缺陷）与死亡之间的关系进行建模，从社交网络分析框架分析它们的相关性。在这里，我们通过结构学习过程和重采样获得的平均贝叶斯网络对 FI 分量和死亡进行建模，以了解 FI 分量和死亡之间的因果关系。我们发现这些组件之间没有类似的相关性，并且缺陷根据其类型而相关。就它们的联系而言，两个缺陷是最相关的，另外两个与死亡直接相关。我们还获得了关系的强度，以确定最合理的、确定高度相关的缺陷集群。最后，我们传播了证据（为所有缺陷分配了值）并研究了 FI 组件如何预测死亡率，获得了近 74% 的正确分配，而敏感性为 56%。作为死亡的分类器，证据中包含的缺陷数量越多，表现越好；但FI似乎不太好正确分类死亡人。我们传播了证据（为所有缺陷分配了值）并研究了 FI 组件如何预测死亡率，获得了近 74% 的正确分配，而敏感性为 56%。作为死亡的分类器，证据中包含的缺陷数量越多，表现越好；但FI似乎不太好正确分类死亡人。我们传播了证据（为所有缺陷分配了值）并研究了 FI 组件如何预测死亡率，获得了近 74% 的正确分配，而敏感性为 56%。作为死亡的分类器，证据中包含的缺陷数量越多，表现越好；但FI似乎不太好正确分类死亡人。