Several algorithms for learning the structure of dynamic Bayesian networks (DBNs) require an a priori ordering of variables, which influences the determined graph topology. However, it is often unclear how to determine this order if feature importance is unknown, especially as an exhaustive search is usually impractical. In this paper, we introduce Ranking Approaches for Unknown Structures (RAUS), an automated framework to systematically inform variable ordering and learn networks end-to-end. RAUS leverages existing statistical methods (Cramers V, chi-squared test, and information gain) to compare variable ordering, resultant generated network topologies, and DBN performance. RAUS enables end-users with limited DBN expertise to implement models via command line interface. We evaluate RAUS on the task of predicting impending acute kidney injury (AKI) from inpatient clinical laboratory data. Longitudinal observations from 67,460 patients were collected from our electronic health record (EHR) and Kidney Disease Improving Global Outcomes (KDIGO) criteria were then applied to define AKI events. RAUS learns multiple DBNs simultaneously to predict a future AKI event at different time points (i.e., 24-, 48-, 72-hours in advance of AKI). We also compared the results of the learned AKI prediction models and variable orderings to baseline techniques (logistic regression, random forests, and extreme gradient boosting). The DBNs generated by RAUS achieved 73-83% area under the receiver operating characteristic curve (AUCROC) within 24-hours before AKI; and 71-79% AUCROC within 48-hours before AKI of any stage in a 7-day observation window. Insights from this automated framework can help efficiently implement and interpret DBNs for clinical decision support. The source code for RAUS is available in GitHub at https://github.com/dgrdn08/RAUS .

翻译：多种动态贝叶斯网络（DBN）结构学习算法要求预先指定变量顺序，该顺序直接影响最终网络拓扑结构。然而，当特征重要性未知时，如何确定最优顺序通常难以明确，尤其是穷举搜索在实践中往往不可行。本文提出未知结构排序方法（RAUS），这是一种自动化框架，可系统化地确定变量顺序并实现端到端的网络学习。RAUS利用现有统计方法（Cramér V系数、卡方检验和信息增益）比较变量排序、生成的网络拓扑结构以及DBN性能，使缺乏DBN专业知识的终端用户可通过命令行界面直接实现模型。我们基于住院患者临床实验室数据，评估RAUS在预测急性肾损伤（AKI）即将发生时的表现。研究收集了67,460名患者的纵向观测数据，依据电子健康记录（EHR）和改善全球肾脏病预后组织（KDIGO）标准定义AKI事件。RAUS可同时学习多个DBN，在不同时间节点（即AKI发生前24小时、48小时、72小时）预测未来AKI事件。我们将学习得到的AKI预测模型及变量排序结果与基线方法（逻辑回归、随机森林和极端梯度提升）进行对比。RAUS生成的DBN在AKI发生前24小时内达到73-83%的受试者工作特征曲线下面积（AUCROC），在7天观测窗口内任意阶段AKI发生前48小时内达到71-79%的AUCROC。该自动化框架的见解有助于高效实现和解释临床决策支持中的DBN。RAUS源代码已发布于GitHub（https://github.com/dgrdn08/RAUS）。