Abstract
Long waiting queues have been a stressful problem in many tertiary public hospitals, which significantly impact the accessibility and quality of health care. One of the key challenges to solve this problem is to provide enough registration windows to serve hospital visit demand under the limited medical and human resources. Traditional window shift scheduling methods are usually based on experiences and biased historical data, which may not accurately reflect the actual hospital visit demand. In this work, we propose a demand-responsive window scheduling framework by accurately modeling and forecasting the fine-grained hospital visit demand from real-world human mobility data. Specifically, in the first phase, we extract hospital visit demand from taxi drop-off events around hospitals, and build a graph model to capture their spatiotemporal patterns. In the second phase, we propose a spatiotemporal graph neural network (ST-GNN) to accurately forecast the hospital visit demand, which simultaneously captures the spatial correlation by graph convolutional networks (GCN) and the temporal dependency by gated recurrent units (GRU). Finally, we exploit a queuing theory model to achieve demand-responsive windows scheduling. Evaluation results using real-world data from Xiamen City show that our framework accurately forecasts hospital visit demand, and effectively schedules hospital registration windows, which consistently outperforms the baselines.
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Mustafa, N., Salim, T.A., Watson, A.: The impact of waiting time on hospital service perception and satisfaction: the moderating role of gender. Int. J. Bus. Manag. Sci. 8(1) (2018)
Qian, Yu., Yang, K.: Hospital registration waiting time reduction through process redesign. Int. J. Six Sigma Compet. Advant. 4(3), 240–253 (2008)
Marcilio, I., Hajat, S., Gouveia, N.: Forecasting daily emergency department visits using calendar variables and ambient temperature readings. Acad. Emerg. Med. 20(8), 769–777 (2013)
Luo, L., Luo, L., Zhang, X., He, X.: Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv. Res. 17(1), 469 (2017)
Taylor, N.B.: The contram dynamic traffic assignment model. Netw. Spatial Econ. 3(3), 297–322 (2003)
Vazquez-Prokopec, G.M., et al.: Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS ONE 8(4), e58802 (2013)
Verdie, Y., Lafarge, F., Alliez, P.: LOD generation for urban scenes. ACM Trans. Graph. 34(ARTICLE), 30 (2015)
Yuan, C., Liu, S., Fang, Z.: Comparison of china’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1, 1) model. Energy 100, 384–390 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., et al.: Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Netw. 2, 985–990 (2004)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Zhou, J., Xu, W.: End-to-end learning of semantic role labeling using recurrent neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (vol. 1: Long Papers), pp. 1127–1137 (2015)
Kleinrock, L.: Queueing Systems. Volume I: Theory (1975)
Box, G.E.P., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Stat. Assoc. 65(332), 1509–1526 (1970)
Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, pp. 155–161 (1997)
Shi, X., Yeung, D.-Y.: Machine Learning for Spatiotemporal Sequence Forecasting: A Survey. arXiv preprint arXiv:1808.06865 (2018)
Ohashi, O., Torgo, L.: Wind speed forecasting using spatio-temporal indicators. In: ECAI, pp. 975–980. Citeseer (2012)
Cliff, A.D., Ord, J.K.: Model building and the analysis of spatial pattern in human geography. J. Roy. Stat. Soc.: Ser. B (Methodol.) 37(3), 297–328 (1975)
Senanayake, R., O’Callaghan, S., Ramos, F.: Predicting spatio-temporal propagation of seasonal influenza using variational Gaussian process regression. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using LSTMs. In: International Conference on Machine Learning, pp. 843–852 (2015)
Xingjian, S.H.I., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Acknowledgement
We would like to thank the reviewers for their constructive suggestions. This research is supported by NSF of China No. 61802325, NSF of Fujian Province No. 2018J01105, and the China Fundamental Research Funds for the Central Universities No. 20720170040.
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Wang, Z., Guo, R., Hong, L., Wang, C., Chen, L. (2020). Demand-Responsive Windows Scheduling in Tertiary Hospital Leveraging Spatiotemporal Neural Networks. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_18
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DOI: https://doi.org/10.1007/978-3-030-64243-3_18
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