Flexible Ureteroscopy Lithotripsy Operative Time Prediction Model for the Treatment of Kidney Stones

Authors

  • Chafik Baidada LTI Laboratory, ENSA, Chouaib Doukkali University, El Jadida, Morocco https://orcid.org/0000-0003-4188-6621
  • Mustapha Aatila LTI Laboratory, ENSA, Chouaib Doukkali University, El Jadida, Morocco https://orcid.org/0000-0002-4330-4579
  • Mohamed Lachgar LTI Laboratory, ENSA, Chouaib Doukkali University, El Jadida, Morocco https://orcid.org/0000-0002-6155-3309
  • Hamid Hrimech Analysis and Modeling of Systems and Decision Support Laboratory, ENSA of Berrechid, Hassan 1er University of Settat, Berrechid, Morocco https://orcid.org/0000-0002-3980-1214
  • Younes Ommane Institute of Biological Sciences (ISSB), UM6P-Faculty of Medical Sciences (UM6P-FMS), Mohammed VI Polytechnic University, Ben-Guerir, Morocco https://orcid.org/0000-0001-6827-2967
  • Abderrahim Houlali Urology Service, Arrazi Hospital, CHU Mohammed VI, Marrakech, Morocco.

DOI:

https://doi.org/10.3991/ijoe.v20i05.43257

Keywords:

Machine learning, flexible ureteroscopy lithotripsy, kidney stones, surgical time prediction, healthcare supply chains

Abstract


Effective time and resource management is crucial not only in the operating room but also in healthcare supply chains. Healthcare supply chains involve the movement of medical supplies, equipment, and medications from manufacturers to healthcare providers. Effective management is crucial to ensuring that patients receive the care they need promptly. In the operating room, it is essential to have an information process in place to effectively manage time and resources during the current surgical procedure. This paper focuses on developing a predictive model for the operating time of flexible ureteroscopy for kidney stones. The model can forecast surgical and preoperative time based on patient characteristics and surgeon experience. The model can assist in planning ureteroscopy procedures and preventing surgical complications, which is crucial not only for the operating room but also for healthcare supply chains. The paper presents a study that compares different feature selection methods and regression techniques. The study found that sequential backward selection combined with the extra tree regressor was the most effective approach.

Cover iJOE – Vol 20, No 05, 2024

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Published

2024-03-15

How to Cite

Baidada, C., Aatila, M., Lachgar, M., Hrimech, H., Ommane, Y., & Abderrahim Houlali. (2024). Flexible Ureteroscopy Lithotripsy Operative Time Prediction Model for the Treatment of Kidney Stones. International Journal of Online and Biomedical Engineering (iJOE), 20(05), pp. 101–119. https://doi.org/10.3991/ijoe.v20i05.43257

Issue

Vol. 20 No. 05 (2024)

Section

Papers

License

Copyright (c) 2024 Chafik BAIDADA, Mohamed LACHGAR, Mustapha AATILA, Hamid HRIMECH, Younes OMMANE, Abderrahim Houlali

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