This paper proposes a novel algorithm for optimizing decision variables with respect to an outcome variable of interest in complex problems, such as those arising from big data. The proposed algorithm builds on the notion of Markov blankets in Bayesian networks to alleviate the computational challenges associated with optimization tasks in complex datasets. Through a case study, we apply the algorithm to optimize medication prescriptions for diabetic patients, who have different characteristics, suffer from multiple comorbidities, and take multiple medications concurrently. In particular, we demonstrate how the optimal combination of diabetic medications can be found by examining the comparative effectiveness of the medications among similar patients. The case study is based on 5 years of data for 19,223 diabetic patients. Our results indicate that certain patient characteristics (e.g., clinical and demographic features) influence optimal treatment decisions. Among patients examined, monotherapy with metformin was the most common optimal medication decision. The results are consistent with the relevant clinical guidelines and reports in the medical literature. The proposed algorithm obviates the need for knowledge of the whole Bayesian network model, which can be very complex in big data problems. The procedure can be applied to any complex Bayesian network with numerous features, multiple decision variables, and a target variable.

本文提出了一种新颖的算法，用于针对诸如大数据引起的复杂问题中的目标结果变量优化决策变量。提出的算法建立在贝叶斯网络中的马尔可夫毯概念上，以减轻与复杂数据集中的优化任务相关的计算挑战。通过案例研究，我们应用该算法为具有不同特征，患有多种合并症并同时服用多种药物的糖尿病患者优化药物处方。特别是，我们证明了如何通过检查相似患者之间药物的比较有效性来找到最佳的糖尿病药物组合。该案例研究基于19223名糖尿病患者的5年数据。我们的结果表明，某些患者特征（例如临床和人口统计学特征）会影响最佳治疗决策。在接受检查的患者中，二甲双胍单药治疗是最常见的最佳用药决策。结果与相关的临床指南和医学文献中的报道一致。所提出的算法消除了对整个贝叶斯网络模型的知识的需求，该知识在大数据问题中可能非常复杂。该过程可以应用于具有众多功能，多个决策变量和一个目标变量的任何复杂贝叶斯网络。结果与相关的临床指南和医学文献中的报道一致。所提出的算法消除了对整个贝叶斯网络模型知识的需求，该知识在大数据问题中可能非常复杂。该过程可以应用于具有众多功能，多个决策变量和一个目标变量的任何复杂贝叶斯网络。结果与相关的临床指南和医学文献中的报道一致。所提出的算法消除了对整个贝叶斯网络模型的知识的需求，该知识在大数据问题中可能非常复杂。该过程可以应用于具有众多功能，多个决策变量和一个目标变量的任何复杂贝叶斯网络。