论文标题
一种自适应模拟的基于退火的机器学习方法，用于开发用于医院紧急操作的电子划分工具
An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations
论文作者
论文摘要
急诊科（ED）的患者分类对于对患有关键和时间敏感病情的患者的护理优先级是必要的。患者分类的工具使用了不同的工具，最常见的工具之一是紧急严重性指数（ESI），该指数的比例为五个级别，其中1级是最紧迫的，而5级是最不紧迫的。本文提出了一个框架，用于利用机器学习开发可以在EDS上使用的电子划分工具。 ED患者就诊的大量回顾性数据集从美国中西部的医疗保健提供商的电子健康记录中获得了三年。但是，使用机器学习算法的主要挑战是，它们中的大多数都有许多参数，而没有优化这些参数，就无法开发高性能模型。本文提出了一种优化机器学习超参数的方法。提出了元启发式优化算法模拟退火（SA）和自适应模拟退火（ASA），以优化极端梯度增强的参数（XGB）和分类增强（CAB）。新提出的算法是SA-XGB，ASA-XGB，SA-CAB，ASA-CAB。网格搜索（GS）是一种用于机器学习微调的传统方法，还用于微调XGB和CAB的参数，这些参数命名为GS-XGB和GS-CAB。使用从特征选择阶段获得的八个数据组对六种算法进行训练和测试。结果表明，ASA-CAB的表现分别以83.3％，83.2％，83.3％，83.2％的精度，精度，召回和F1优于所有提出的算法。
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.
