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Predictive reliability and validity of hospital cost analysis with dynamic neural network and genetic algorithm

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A Correction to this article was published on 12 May 2020

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Abstract

Hospital cost analysis (HCA) becomes a key topic and forefront of politics, social welfare and medical discourse. HCA includes a wide range of expenses; yet the foremost attention relates to the money expense in which hospital managers would like to draw a figure of incomes in the past and future. Based on the HCA results, they can develop many plans for improving hospital’s service quality and investing in potential healthcare services in order to deliver better services with lower costs. Machine learning methods are often opted for prediction in HCA. In this paper, we propose a new method for HCA that uses genetic algorithm (GA) and artificial neural network (ANN). Operators of GA are used to boost up calculation to get optimal weights in the forward propagation of ANN. Experiments on a real database of Hanoi Medical University Hospital (HMUH) including calculus of kidney and ureter inpatients show that the new method achieves better accuracy than the relevant ones including linear regression, K-nearest neighbors (KNN), ANN and deep learning. The mean squared error of the proposed model gets the lowest value (0.00360), compared to those of deep learning, KNN and linear regression which are 0.00901, 0.01205 and 0.01718 respectively.

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  • 12 May 2020

    In the original article, family name of one of the co-authors (Duong Thi Thu Huyen) has been missed in the online publication.

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Acknowledgements

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant Number 102.05-2018.02. The authors wish to acknowledge the board of directors of Hanoi Medical University Hospital, Vietnam for giving us the materials needed for this research.

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Correspondence to Le Hoang Son.

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Son, L.H., Ciaramella, A., Thu Huyen, D.T. et al. Predictive reliability and validity of hospital cost analysis with dynamic neural network and genetic algorithm. Neural Comput & Applic 32, 15237–15248 (2020). https://doi.org/10.1007/s00521-020-04876-w

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