Patient Treatment Time Prediction in Hospital Queuing Management

Authors(2) :-Swapnil Jagtap, Prof.Majusha Tatiya

Hospitals patient queue and its management to lower patient waits delays daily is considered as challenging tasks. Time wasting waits for long period results in to poor service and also lowers the hospitals reputation. Patient waiting for the treatment task to be completed need to wait for all the other patients who are appointed before him. This all the factors can be avoided if patient receive the updates about the queue, required time he need to wait for on his mobile phone. Understanding these problems faced by the hospitals, I proposed an individual Parallel Treatment Time Prediction (PTTP) system which will be responsible for analyzing the waiting period for every treatment activity for a patient. Here the realistic patient data from hospitals will be analyzed to calculate estimated patient treatment time for every task. The system will be responsible for getting the updates regarding huge and realtime data-set, the treatments time taken by each and patient among the list of present queue of every task is analyzed. With the successful recognition about the time taken by patient and waiting time, the Hospital Queuing Recommendation (HQR) system is implemented. HQR responsible for the suggestion about the time efficient treatment tasks for the individual. The models thus helps us to overcome the problems faced by hospitals with the help of HQRs time efficiency.

Authors and Affiliations

Swapnil Jagtap
Computer Engineering, Indira College of Engineering & Management, Pune, Maharashtra, India
Prof.Majusha Tatiya
Computer Engineering, Indira College of Engineering & Management, Pune, Maharashtra, India

Treatment time, Hospital queuing recommendation, Patient treatment time prediction, Hospital waiting’s, Patient queue, Hospital queue portal.

  1. Jiangua Chen, Kenli Li, Zhuo Tang, Kashif Bilal, Keqin Li, A Parallel, “Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation in a Big Data Environment”, Digital Object Identifier 10.1109/ACCESS.2016.2558199, April, 2016.
  2. R. Fidalgo-Merino and M. Nunez, “Self-adaptive induction of regression trees”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 16591672, Aug. 2011.
  3. S. Tyree, K. Q. Weinberger, K. Agrawal, and J. Paykin, “Parallel boosted regression trees for Web search ranking”, in Proc. 20th Int. Conf. World Wide Web (WWW), 2012, pp. 387396.
  4. S. Meng, W. Dou, X. Zhang, and J. Chen, “KASR: A keyword-aware service recommendation method on MapReduce for big data applications”, IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 12, pp. 32213231, Dec. 2014.
  5. N. T. Van Uyen and T. C. Chung, “A new framework for distributed boosting algorithm”, in Proc. Future Generat. Commun. Netw. (FGCN), Dec. 2007, pp. 420423.
  6. G. Yu, N. A. Goussies, J. Yuan, and Z. Liu, “Fast action detection via discriminative random forest voting and top-K sub volume search”, IEEE Trans. Multimedia, vol. 13, no. 3, pp. 507517, Jun. 2011.
  7. N. Salehi-Moghaddami, H. S. Yazdi, and H. Poostchi, “Correlation based splitting criterion in multi branch decision tree”, Central Eur. J. Comput.Sci., vol. 1, no. 2, pp. 205220, Jun. 2011.
  8. G. Chrysos, P. Dagritzikos, I. Papaefstathiou, and A. Dollas, “HC-CART: A parallel system implementation of data mining classification and regression tree (CART) algorithm on a multi-FPGA system”, ACM Trans. Archit. Code Optim., vol. 9, no. 4, pp. 47:147:25, Jan. 2013
  9. C. Lindner, P. A. Bromiley, M. C. Ionita, and T. F. Cootes, “Robust and accurate shape model matching using random forest regression-voting”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 9, pp. 18621874, Sep. 2015.
  10.  ] Y. Ben-Haim and E. Tom-Tov, “A streaming parallel decision tree algorithm”, J. Mach. Learn. Res., vol. 11, no. 1, pp. 849872, Oct. 2010.
  11. L. Breiman, “Random forests, Mach. Learn.”, vol. 45, no. 1, pp. 532, Oct. 2001.
  12. “A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment” Jianguo Chen, Kenli Li, Senior Member, IEEE, Zhuo Tang, Member, IEEE, Kashif Bilal, Shui Yu, Member, IEEE, Chuliang Weng, Member, IEEE, and Keqin Li, Fellow, IEEE, 1045-9219 (c) 2016 IEEE.
  13. X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, “Data mining with big data”, IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97-107, Jan. 2014.
  14. G. Adomavicius and Y. Kwon, “New recommendation techniques for multicriteria rating systems,” IEEE Intell. Syst., vol. 22, no. 3, pp. 48, 55, May/Jun. 2007.
  15. X. Yang, Y. Guo, and Y. Liu, “Bayesian-inference-based recommendation in online social networks,” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 4, pp. 642-651, Apr. 2013.

Publication Details

Published in : Volume 4 | Issue 8 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 482-490
Manuscript Number : IJSRSET1848137
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Swapnil Jagtap, Prof.Majusha Tatiya, " Patient Treatment Time Prediction in Hospital Queuing Management, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 8, pp.482-490, May-June-2018.
Journal URL :

Article Preview