基于聚类算法的医疗预约调度模型
Appointment scheduling model in healthcare using clustering algorithms
AI总结：
本研究提出结合聚类算法与数学规划的医疗预约调度模型，通过四种聚类方法划分门诊患者优先级，并利用流体近似求解MDP，以最小化高优先级患者的等待时间。
摘要：
在本研究中，我们提出了一种结合机器学习和数学规划的调度流程，旨在最小化高优先级门诊患者的等待时间。在医疗机构中请求预约的门诊患者具有不同的优先级。确定门诊患者的优先级并根据优先级类别分配容量，是在门诊患者调度中必须考虑的重要概念。在本研究中，为调度新来的门诊患者定义了两个阶段。在初始阶段，我们采用并评估了四种不同的聚类技术：K-means聚类、凝聚层次聚类、DBSCAN和OPTICS聚类，以将门诊患者划分为优先级类别，并提出了最佳的聚类模式。在第二阶段，我们将调度问题建模为马尔可夫决策过程（MDP）问题，因为到达具有不确定性，且决策在每天结束时观察总请求后做出。由于维数灾难，我们使用流体近似方法来估计MDP的最优解。我们的方法应用于Shaheed Rajaei医疗研究中心的数据库，并展示了我们的模型在门诊患者优先级划分和调度中的工作方式。
英文摘要：
In this study, we provided a scheduling procedure which is a combination of machine learning and mathematical programming that minimizes the waiting time of higher priority outpatients. Outpatients who request for appointment in healthcare facilities have different priorities. Determining the priority of outpatients and allocating the capacity based on the priority classes are important concepts that have to be considered in the scheduling of outpatients. In this study, two stages are defined for scheduling an incoming outpatient. In the initial stage, we employed and evaluated four distinct clustering techniques; K-means clustering, agglomerative hierarchical clustering, DBSCAN, and OPTICS clustering to classify outpatients into priority classes and suggested the best pattern to cluster the outpatients. In the second stage, we modeled the scheduling problem as a Markov Decision Process (MDP) problem since the arrivals are uncertain and the decisions are taken at the end of each day after observing total requests. Due to the curse of dimensionality, we used the fluid approximation method to estimate the optimal solution of the MDP. our methodology is employed in a data set of Shaheed Rajaei Medical and Research Center, and we represented how our model works in prioritizing and scheduling outpatients.
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