In this paper, a deep belief network (DBN) is employed to deal with the problem of the patient attendance disposal in accident & emergency (A&E) departments. The selection of the hyperparameters of the employed DBN is automated by using the particle swarm optimization (PSO) algorithm that is known for its simplicity, easy implementation and relatively fast convergence rate to a satisfactory solution. Specifically, a recently developed randomly occurring distributedly delayed PSO (RODDPSO) algorithm, which is capable of seeking the optimal solution and alleviating the premature convergence, is exploited with aim to optimize the hyperparameters of the DBN. The developed RODDPSO-based DBN is successfully applied to analyze the A&E data for classifying the patient attendance disposal in the A&E department of a hospital in west London. Experimental results show that the proposed RODDPSO-based DBN outperforms the standard DBN and the modified DBN in terms of the classification accuracy.

本文采用深度信念网络（DBN）来处理急诊（A＆E）部门中的患者出勤处置问题。通过使用粒子群优化（PSO）算法，可以自动选择所用DBN的超参数，该算法以其简单，易于实现和相对快速的收敛速度而著称于令人满意的解决方案。具体地，为了优化DBN的超参数，开发了能够寻求最佳解决方案并减轻过早收敛的，最近开发的随机发生的分布式延迟PSO（RODDPSO）算法。基于RODDPSO的已开发DBN已成功地应用于分析伦敦西部一家医院的急诊科的急诊数据以对患者的出勤情况进行分类。