This work proposes a framework for optimizing machine learning algorithms.
The practicality of the framework is illustrated using an important case study
from the healthcare domain, which is predicting the admission status of
emergency department (ED) patients (e.g., admitted vs. discharged) using
patient data at the time of triage. The proposed framework can mitigate the
crowding problem by proactively planning the patient boarding process. A large
retrospective dataset of patient records is obtained from the electronic health
record database of all ED visits over three years from three major locations of
a healthcare provider in the Midwest of the US. Three machine learning
algorithms are proposed: T-XGB, T-ADAB, and T-MLP. T-XGB integrates extreme
gradient boosting (XGB) and Tabu Search (TS), T-ADAB integrates Adaboost and
TS, and T-MLP integrates multi-layer perceptron (MLP) and TS. The proposed
algorithms are compared with the traditional algorithms: XGB, ADAB, and MLP, in
which their parameters are tunned using grid search. The three proposed
algorithms and the original ones are trained and tested using nine data groups
that are obtained from different feature selection methods. In other words, 54
models are developed. Performance was evaluated using five measures: Area under
the curve (AUC), sensitivity, specificity, F1, and accuracy. The results show
that the newly proposed algorithms resulted in high AUC and outperformed the
traditional algorithms. The T-ADAB performs the best among the newly developed
algorithms. The AUC, sensitivity, specificity, F1, and accuracy of the best
model are 95.4%, 99.3%, 91.4%, 95.2%, 97.2%, respectively.

本文提出了一种基于机器学习算法优化的框架，以急诊部门的患者入院情况为例进行实证研究，利用大规模的患者数据和三种新的机器学习算法对传统算法进行对比，结果表明新算法在 AUC 等五个指标上表现更好，其中 T-ADAB 算法效果最佳，最优模型的 AUC、灵敏度、特异度、F1 和准确率分别达到 95.4%、99.3%、91.4%、95.2% 和 97.2%。