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

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Abstract

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.

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References

  1. Bengio Y, Lamblin P, Popovici D, Larochelle H (2006) Greedy layerwise training of deep networks. In: Proceedings of advances in neural information processing systems 19, Vancouver, BC, Canada, pp 153–160

  2. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

    Article  MathSciNet  Google Scholar 

  3. Bergstra JS, Bardenet R, Bengio Y, Kegl B (2011) Algorithms for hyper-parameter optimization. In: Proceedings of advances in neural information processing systems 24, Vancouver, BC, Canada, pp 2546–2554

  4. Bhattacharjee P, Ray PK (2014) Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: a review and reflections. Comput Ind Eng 78:299–312

    Article  Google Scholar 

  5. Chen D, Chen W, Hu J, Liu H (2019) Variance-constrained filtering for discrete-time genetic regulatory networks with state delay and random measurement delay. Int J Syst Sci 50(2):231–243

    Article  MathSciNet  Google Scholar 

  6. Cheng H, Wang Z, Wei Z, Ma L, Liu X (2020) On adaptive learning framework for deep weighted sparse autoencoder: a multiobjective evolutionary algorithm. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3009582

  7. Cui L, Li G, Lin Q, Du Z, Gao W, Chen J, Lu N (2016) A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation. Inf Sci 367:1012–1044

    Article  Google Scholar 

  8. Cui L, Li G, Lin Q, Chen J, Lu N (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput Oper Res 67:155–173

    Article  MathSciNet  Google Scholar 

  9. Eatock J, Clarke M, Picton C, Young T (2011) Meeting the four-hour deadline in an A&E department. J Health Org Manag 25(6):606–624

    Article  Google Scholar 

  10. Geng H, Wang Z, Liang Y, Cheng Y, Alsaadi FE (2017) Tobit Kalman filter with time-correlated multiplicative sensor noises under redundant channel transmission. IEEE Sens J 17(24):8367–8377

    Article  Google Scholar 

  11. Gong M, Liu J, Li H, Cai Q, Su L (2015) A multiobjective sparse feature learning model for deep neural networks. IEEE Trans Neural Netw Learn Syst 26(12):3263–3277

    Article  MathSciNet  Google Scholar 

  12. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    MATH  Google Scholar 

  13. Gul M, Guneri AF (2015) A comprehensive review of emergency department simulation applications for normal and disaster conditions. Comput Ind Eng 83:327–344

    Article  Google Scholar 

  14. Hinton G, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  Google Scholar 

  15. Hu J, Wang Z, Liu G-P, Jia C, Williams J (2020) Event-triggered recursive state estimation for dynamical networks under randomly switching topologies and multiple missing measurements. Automatica 115:108908

  16. Hu J, Wang Z, Liu G-P, Jia C, Zhang H (2020) Variance-constrained recursive state estimation for time-varying complex networks with quantized measurements and uncertain inner coupling. IEEE Trans Neural Netw Learn Syst 31(6):1955–1967

    Article  MathSciNet  Google Scholar 

  17. Jiang S, Chin K-S, Wang L, Qu G, Tsui KL (2017) Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Expert Syst Appl 82:216–230

    Article  Google Scholar 

  18. Jiang S, Chin K-S, Tsui KL (2018) A universal deep learning approach for modeling the flow of patients under different severities. Comput Methods Programs Biomed 154:191–203

    Article  Google Scholar 

  19. Kienzle W, Chellapilla K (2006) Personalized handwriting recognition via biased regularization. In: Proceedings of the 23rd international conference on machine learning, Pennsylvania, USA, pp 457–464

  20. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  21. Liu H, Wang Z, Fei W, Li J, Alsaadi FE (2021) On inite-horizon H-infinity state estimation for discrete-time delayed memristive neural networks under stochastic communication protocol. Inf Sci 555:280–292

    Article  Google Scholar 

  22. Liu N, Koh ZX, Chua ECP, Tan LML, Lin Z, Mirza B, Ong MEH (2014) Risk scoring for prediction of acute cardiac complications from imbalanced clinical data. IEEE J Biomed Health Informatics 18(6):1894–1902

    Article  Google Scholar 

  23. Liu S, Wang Z, Wang L, Wei G (2018) On quantized \(H_{\infty }\) filtering for multi-rate systems under stochastic communication protocols: the finite-horizon case. Inf Sci 459:211–223

    Article  Google Scholar 

  24. Liu S, Wang Z, Wei G, Li M (2020) Distributed set-membership filtering for multirate systems under the Round-Robin scheduling over sensor networks. IEEE Trans Cybern 50(5):1910–1920

    Article  Google Scholar 

  25. Liu W, Wang Z, Liu X, Zeng N, Bell D (2019) A novel particle swarm optimization approach for patient clustering from emergency departments. IEEE Trans Evol Comput 23(4):632–644

    Article  Google Scholar 

  26. Liu W, Wang Z, Yuan Y, Zeng N, Hone K, Liu X (2021) A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE Trans Cybern 51(2):1085–1093

    Article  Google Scholar 

  27. Liu W, Wang Z, Zeng N, Yuan Y, Alsaadi FE, Liu X (2021) A novel randomised particle swarm optimizer. Int J Mach Learn Cybern 12(2):529–540

    Article  Google Scholar 

  28. Luo X, Yuan Y, Chen S, Zeng N, Wang Z (2020) Position-transitional particle swarm optimization-incorporated latent factor analysis. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2020.3033324

  29. Luo X, Yuan Y, Zhou M, Liu Z, Shang M (2019) Non-negative latent factor model based on \(\beta \)-divergence for recommender systems. IEEE Trans System Man Cybern Syst. https://doi.org/10.1109/TSMC.2019.2931468

  30. Mohamed AR, Dahl G, Hinton G (2009) Deep belief networks for phone recognition. In: Proceedings of NIPS workshop on deep learning for speech recognition and related applications

  31. Papa JP, Scheirer W, Cox DD (2016) Fine-tuning deep belief networks using harmony search. Appl Soft Comput 46:875–885

    Article  Google Scholar 

  32. Passos LA, Rodrigues DR, Papa JP (2018) Fine tuning deep boltzmann machines through meta-heuristic approaches. In: Proceedings of IEEE 12th international symposium on applied computational intelligence and informatics, Timisoara, Romania, pp 419–424

  33. Raita Y, Goto T, Faridi MK, Brown DF, Camargo CA, Hasegawa K (2019) Emergency department triage prediction of clinical outcomes using machine learning models. Critical Care 23:64

  34. Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Article  Google Scholar 

  35. Sheng W, Shan P, Chen S, Liu Y, Alsaadi FE (2017) A niching evolutionary algorithm with adaptive negative correlation learning for neural network ensemble. Neurocomputing 247:173–182

    Article  Google Scholar 

  36. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 IEEE congress on evolutionary computation, Washington, DC, USA, pp 1945–1950

  37. Song B, Wang Z, Zou L (2017) On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm. Cogn Comput 9(1):5–17

    Article  Google Scholar 

  38. Tan PN, Steinbach M, Kumar V (2005) Introduction to data mining. Addison Wesley, Boston

    Google Scholar 

  39. Tang F, Mao B, Fadlullah ZM, Liu J, Kato N (2020) ST-DeLTA: an novel spatial-temporal value network aided deep learning based intelligent network traffic control system. IEEE Trans Sustain Comput 5(4):568–580

    Article  Google Scholar 

  40. Wang L, Wang Z, Wei G, Alsaadi FE (2019) Observer-based consensus control for discrete-time multi-agent systems with coding-decoding communication protocol. IEEE Trans Cybern 49(12):4335–4345

    Article  Google Scholar 

  41. Xiao X, Dow ER, Eberhart R, Miled ZB, Oppelt RJ (2003) Gene clustering using self-organizing maps and particle swarm optimization. In: Proceedings of the international parallel and distributed processing symposium, Nice, France

  42. Yue W, Wang Z, Tian B, Payne A, Liu X (2020) A collaborative-filtering-based data collection strategy for Friedreich’s ataxia. Cogn Comput 12:249–260

    Article  Google Scholar 

  43. Yue W, Wang Z, Liu W, Tian B, Lauria S, Liu X (2020) An optimally weighted user-and item-based collaborative filtering approach to predicting baseline data for Friedreich’s ataxia patients. Neurocomputing 419:287–294

    Article  Google Scholar 

  44. Yue W, Wang Z, Tian B, Pook M, Liu X (2021) A hybrid model- and memory-based collaborative filtering algorithm for baseline data prediction of Friedreich’s ataxia patients. IEEE Trans Industr Inf 17(2):1428–1437

    Article  Google Scholar 

  45. Zeng N, Wang Z, Zhang H, Alsaadi FE (2016) A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay. Cogn Comput 8(2):143–152

    Article  Google Scholar 

  46. Zeng N, Wang Z, Liu W, Zhang H, Hone K, Liu X (2020) A dynamic neighborhood-based switching particle swarm optimization algorithm. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3029748

  47. Zhan Z-H, Zhang J, Li Y, Chung HS-H (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39(6):1362–1381

    Article  Google Scholar 

  48. Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237

    Article  Google Scholar 

  49. Zhao Z, Wang Z, Zou L, Guo J (2020) Set-Membership filtering for time-varying complex networks with uniform quantisations over randomly delayed redundant channels. Int J Syst Sci 51(16):3364–3377

    Article  MathSciNet  Google Scholar 

  50. Zou L, Wang Z, Hu J, Zhou D (2020) Moving horizon estimation with unknown inputs under dynamic quantization effects. IEEE Trans Autom Control 65(12):5368–5375

    Article  MathSciNet  Google Scholar 

  51. Zou L, Wang Z, Zhou D (2020) Moving horizon estimation with non-uniform sampling under component-based dynamic event-triggered transmission. Automatica 120:13

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 61873148 and 61933007, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany.

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Authors and Affiliations

  1. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Weibo Liu, Zidong Wang & Xiaohui Liu

  2. Department of Instrumental and Electrical Engineering, Xiamen University, Fujian, 361005, China

    Nianyin Zeng

  3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Fuad E. Alsaadi

Authors
  1. Weibo Liu
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  2. Zidong Wang
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  3. Nianyin Zeng
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  4. Fuad E. Alsaadi
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  5. Xiaohui Liu
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Correspondence to Zidong Wang.

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Liu, W., Wang, Z., Zeng, N. et al. A PSO-based deep learning approach to classifying patients from emergency departments. Int. J. Mach. Learn. & Cyber. 12, 1939–1948 (2021). https://doi.org/10.1007/s13042-021-01285-w

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  • DOI: https://doi.org/10.1007/s13042-021-01285-w

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