A PSO-based deep learning approach to classifying patients from emergency departments

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.

Automated summary

This study employs a deep belief network to address patient attendance disposal in A&E departments, with the hyperparameters selection being automated by a PSO algorithm. The RODDPSO algorithm is utilized to optimize the hyperparameters of the DBN, achieving better classification accuracy compared to standard and modified DBNs in analyzing A&E data from a hospital in west London.