Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. Different tools are used for patient triage and one of the most common ones is the emergency severity index (ESI), which has a scale of five levels, where level 1 is the most urgent and level 5 is the least urgent. This paper proposes a framework for utilizing machine learning to develop an e-triage tool that can be used at EDs. A large retrospective dataset of ED patient visits is obtained from the electronic health record of a healthcare provider in the Midwest of the US for three years. However, the main challenge of using machine learning algorithms is that most of them have many parameters and without optimizing these parameters, developing a high-performance model is not possible. This paper proposes an approach to optimize the hyperparameters of machine learning. The metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The newly proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, ASA-CaB. Grid search (GS), which is a traditional approach used for machine learning fine-tunning is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The six algorithms are trained and tested using eight data groups obtained from the feature selection phase. The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, 83.2%, respectively.
翻译:急诊部门(EDs)的病人切换需要急诊部门(EDs)的病人切换手术,以便优先护理具有关键和时间敏感条件的病人。对病人切换手术使用不同的工具,最常用的工具之一是紧急重力指数(ESI),该指数有五个等级,其中一级最为紧迫,第5级最不紧迫。本文提出一个框架,用于利用机器学习开发电子切换工具,该工具可用于EDs。从美国中西部一个医疗保健提供者的电子健康记录中获取大量ED病人访问的追溯数据集,为期三年。然而,使用机器精确度算法的主要挑战是,其中多数具有许多参数,而且没有优化这些参数,开发高性能模型是不可能的。本文提出了一种优化机器学习超参数的方法。脑力优化算法模拟了nealing(SA)和适应性模拟内纳(ASA)的模拟变异化(X)和直线推进(Cab)的变异化(SA)的参数是SA-X值方法,A-X的高级算法是A-X级算法,A-X的检索是A-X的A-X GBA-C 也使用了AS-GBA。