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Genetic Algorithm for Patient Assignment Optimization in Cloud Healthcare System

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Health Information Science (HIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13705))

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Abstract

The cloud healthcare system is achieved based on the integration between Internet technologies and the traditional healthcare system. By combining online diagnosis and offline treatment, such a system can effectively reduce patients’ waiting time and also improve idle medical resources’ utilization ratio. In this paper, to optimize the balance of patient assignment (PA) in the cloud healthcare system, a genetic algorithm (GA) is proposed. Each individual in the proposed GA represents a solution for the PA optimization problem. Better solutions are generated by executing crossover, mutation, and selection operators in GA. Experiments verify that the proposed GA is effective in optimizing the PA problem.

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References

  1. Barros, P.P., Olivella, P.: Waiting lists and patient selection. J. Econ. Manage. Strategy 14(3), 623–646 (2005). https://doi.org/10.1111/j.1530-9134.2005.00076.x

    Article  Google Scholar 

  2. Chawasemerwa, T., Taifa, I., Hartmann, D.: Development of a doctor scheduling system: a constraint satisfaction and penalty minimisation scheduling model. Int. J. Res. Ind. Eng. 7(4), 396–422 (2018). https://doi.org/10.22105/riej.2018.160257.1068

  3. Chen, Z.G., Zhan, Z.H., Wang, H., Zhang, J.: Distributed individuals for multiple peaks: a novel differential evolution for multimodal optimization problems. IEEE Trans. Evol. Comput. 24(4), 708–719 (2020). https://doi.org/10.1109/tevc.2019.2944180

    Article  Google Scholar 

  4. Conforti, D., Guerriero, F., Guido, R.: Optimization models for radiotherapy patient scheduling. 4Or, 6(3), 263–278 (2007). https://doi.org/10.1007/s10288-007-0050-8

  5. Du, J., Michalska, S., Subramani, S., Wang, H., Zhang, Y.: Neural attention with character embeddings for hay fever detection from Twitter. Health Inf. Sci. Syst. 7(1), 1–7 (2019). https://doi.org/10.1007/s13755-019-0084-2

    Article  Google Scholar 

  6. Ge, Y.F., et al.: A benefit-driven genetic algorithm for balancing privacy and utility in database fragmentation. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 771–776. ACM (2019). https://doi.org/10.1145/3321707.3321778

  7. Ge, Y.-F., Cao, J., Wang, H., Zhang, Y., Chen, Z.: Distributed differential evolution for anonymity-driven vertical fragmentation in outsourced data storage. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2020. LNCS, vol. 12343, pp. 213–226. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62008-0_15

    Chapter  Google Scholar 

  8. Ge, Y.F., Orlowska, M., Cao, J., Wang, H., Zhang, Y.: Knowledge transfer-based distributed differential evolution for dynamic database fragmentation. Knowl.-Based Syst. 229, 107325 (2021). https://doi.org/10.1016/j.knosys.2021.107325

    Article  Google Scholar 

  9. Ge, Y.F., Orlowska, M., Cao, J., Wang, H., Zhang, Y.: MDDE: multitasking distributed differential evolution for privacy-preserving database fragmentation. VLDB J. 31, 1–19 (2021). https://doi.org/10.1007/s00778-021-00718-w

    Article  Google Scholar 

  10. Ge, Y.F., et al.: Distributed memetic algorithm for outsourced database fragmentation. IEEE Trans. Cybern. 51(10), 4808–4821 (2021). https://doi.org/10.1109/tcyb.2020.3027962

    Article  Google Scholar 

  11. Ge, Y.F., Yu, W.J., Zhan, Z.H., Zhang, J.: Competition-based distributed differential evolution. In: 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE (2018). https://doi.org/10.1109/cec.2018.8477758

  12. Ge, Y.F., Yu, W.J., Zhang, J.: Diversity-based multi-population differential evolution for large-scale optimization. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ACM (2016). https://doi.org/10.1145/2908961.2908995

  13. Gijo, E.V., Antony, J.: Reducing patient waiting time in outpatient department using lean six sigma methodology. Qual. Reliab. Eng. Int. 30(8), 1481–1491 (2013). https://doi.org/10.1002/qre.1552

    Article  Google Scholar 

  14. He, J., Rong, J., Sun, L., Wang, H., Zhang, Y., Ma, J.: A framework for cardiac arrhythmia detection from IoT-based ECGs. World Wide Web 23(5), 2835–2850 (2020). https://doi.org/10.1007/s11280-019-00776-9

    Article  Google Scholar 

  15. Hossain, N.U.I., Debusk, H., Hasan, M.M.: Reducing patient waiting time in an outpatient clinic: a discrete event simulation (DES) based approach. In: Proceedings of IIE Annual Conference, pp. 241–246. Institute of Industrial and Systems Engineers (IISE) (2017)

    Google Scholar 

  16. Jiang, H., Zhou, R., Zhang, L., Wang, H., Zhang, Y.: Sentence level topic models for associated topics extraction. World Wide Web 22(6), 2545–2560 (2018). https://doi.org/10.1007/s11280-018-0639-1

    Article  Google Scholar 

  17. Lee, J., Park, J., Wang, K., Feng, B., Tennant, M., Kruger, E.: The use of telehealth during the coronavirus (COVID-19) pandemic in oral and maxillofacial surgery - a qualitative analysis. ICST Trans. Scalable Inf. Syst. 9, 172361 (2021). https://doi.org/10.4108/eai.2-12-2021.172361

    Article  Google Scholar 

  18. Li, J.Y., Du, K.J., Zhan, Z.H., Wang, H., Zhang, J.: Distributed differential evolution with adaptive resource allocation. IEEE Trans. Cybern. (2022). https://doi.org/10.1109/tcyb.2022.3153964

    Article  Google Scholar 

  19. Li, Y., Wang, H., Li, Y., Li, L.: Patient assignment scheduling in a cloud healthcare system based on petri net and greedy-based heuristic. Enterp. Inf. Syst. 13(4), 515–533 (2018). https://doi.org/10.1080/17517575.2018.1526323

    Article  Google Scholar 

  20. Mardiah, F.P., Basri, M.H.: The analysis of appointment system to reduce outpatient waiting time at Indonesia’s public hospital. Hum. Resour. Manage. Res. 3(1), 27–33 (2013)

    Google Scholar 

  21. Mirjalili, S.: Evolutionary Algorithms and Neural Networks. SCI, vol. 780. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93025-1

  22. Munavalli, J.R., Rao, S.V., Srinivasan, A., van Merode, G.: Integral patient scheduling in outpatient clinics under demand uncertainty to minimize patient waiting times. Health Inform. J. 26(1), 435–448 (2019). https://doi.org/10.1177/1460458219832044

    Article  Google Scholar 

  23. Pandey, D., Wang, H., Yin, X., Wang, K., Zhang, Y., Shen, J.: Automatic breast lesion segmentation in phase preserved DCE-MRIs. Health Inf. Sci. Syst. 10(1), 1–19 (2022). https://doi.org/10.1007/s13755-022-00176-w

    Article  Google Scholar 

  24. Patrick, J., Puterman, M.L., Queyranne, M.: Dynamic multipriority patient scheduling for a diagnostic resource. Oper. Res. 56(6), 1507–1525 (2008). https://doi.org/10.1287/opre.1080.0590

    Article  MathSciNet  MATH  Google Scholar 

  25. Price, K.V.: Differential evolution. In: Zelinka, I., Snášel, V., Abraham, A. (eds.) Handbook of Optimization, pp. 187–214. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-30504-7_8

  26. Sarki, R., Ahmed, K., Wang, H., Zhang, Y.: Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf. Sci. Syst. 8(1), 1–9 (2020). https://doi.org/10.1007/s13755-020-00125-5

    Article  Google Scholar 

  27. Sarki, R., Ahmed, K., Wang, H., Zhang, Y., Wang, K.: Convolutional neural network for multi-class classification of diabetic eye disease. ICST Trans. Scalable Inf. Syst. 9, 172436 (2021). https://doi.org/10.4108/eai.16-12-2021.172436

    Article  Google Scholar 

  28. Singh, R., Zhang, Y., Wang, H., Miao, Y., Ahmed, K.: Investigation of social behaviour patterns using location-based data - a Melbourne case study. ICST Trans. Scalable Inf. Syst. 8, 166767 (2020). https://doi.org/10.4108/eai.26-10-2020.166767

    Article  Google Scholar 

  29. Siuly, S., et al.: A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 28(9), 1966–1976 (2020). https://doi.org/10.1109/tnsre.2020.3013429

    Article  Google Scholar 

  30. Srinivas, M., Patnaik, L.: Genetic algorithms: a survey. Computer 27(6), 17–26 (1994). https://doi.org/10.1109/2.294849

    Article  Google Scholar 

  31. Supriya, S., Siuly, S., Wang, H., Zhang, Y.: Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf. Sci. Syst. 8(1), 1–15 (2020). https://doi.org/10.1007/s13755-020-00129-1

    Article  Google Scholar 

  32. Takakuwa, S., Wijewickrama, A.: Optimizing staffing schedule in light of patient satisfaction for the whole outpatient hospital ward. In: 2008 Winter Simulation Conference. IEEE (2008). https://doi.org/10.1109/wsc.2008.4736230

  33. Vimalachandran, P., Liu, H., Lin, Y., Ji, K., Wang, H., Zhang, Y.: Improving accessibility of the Australian my health records while preserving privacy and security of the system. Health Inf. Sci. Syst. 8(1), 1–9 (2020). https://doi.org/10.1007/s13755-020-00126-4

    Article  Google Scholar 

  34. Wang, Z.J., et al.: Automatic niching differential evolution with contour prediction approach for multimodal optimization problems. IEEE Trans. Evol. Comput. 24(1), 114–128 (2020). https://doi.org/10.1109/tevc.2019.2910721

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Guangdong Technion Israel Institute of Technology, Shantou, China

    Xinyu Pang

  2. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia

    Yong-Feng Ge

  3. School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia

    Kate Wang

Authors
  1. Xinyu Pang
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  2. Yong-Feng Ge
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  3. Kate Wang
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Corresponding author

Correspondence to Yong-Feng Ge .

Editor information

Editors and Affiliations

  1. University of São Paulo, São Carlos, Brazil

    Agma Traina

  2. Victoria University, Melbourne, VIC, Australia

    Hua Wang

  3. Tsinghua University, Beijing, China

    Yong Zhang

  4. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  6. Swinburne University of Technology, Hawthorn, VIC, Australia

    Lu Chen

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Pang, X., Ge, YF., Wang, K. (2022). Genetic Algorithm for Patient Assignment Optimization in Cloud Healthcare System. In: Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L. (eds) Health Information Science. HIS 2022. Lecture Notes in Computer Science, vol 13705. Springer, Cham. https://doi.org/10.1007/978-3-031-20627-6_19

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  • DOI: https://doi.org/10.1007/978-3-031-20627-6_19

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  • Print ISBN: 978-3-031-20626-9

  • Online ISBN: 978-3-031-20627-6

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