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Using KDD process to forecast a duration of surgery

Author

Listed:
  • Catherine Combes

    (LabHC - Laboratoire Hubert Curien - IOGS - Institut d'Optique Graduate School - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Nadine Meskens

    (Département Gestion de Production et des Opérations - FUCAM - Facultés Universitaires Catholiques de Mons)

  • Celine Rivat

    (COACTIS - COnception de l'ACTIon en Situation - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne)

Abstract

This paper presents a methodological framework for planning surgery in operating theatre suites based on data warehousing and knowledge discovery in database approaches. We suggest a decisional tool which estimates the appropriate duration for a patient to be in the operating theatre. To achieve this, we first describe a data warehouse model used to extract data from various, possibly non-interacting, databases. Then we compare two data mining methods: rough sets and neural networks. The aim is to identify classes of surgery likely to take different lengths of time according to the patient's profile. These tools permit patients profiles to be identified from administrative data, previous medical history, etc. The surgical environment (surgeon, type of anesthesia, etc.) is also taken into account in estimating the duration of the surgery.

Suggested Citation

  • Catherine Combes & Nadine Meskens & Celine Rivat, 2008. "Using KDD process to forecast a duration of surgery," Post-Print halshs-00519264, HAL.
    • Handle: RePEc:hal:journl:halshs-00519264
    as

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    Citations

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    Cited by:

    1. Lamiri, Mehdi & Grimaud, Frédéric & Xie, Xiaolan, 2009. "Optimization methods for a stochastic surgery planning problem," International Journal of Production Economics, Elsevier, vol. 120(2), pages 400-410, August.
    2. Wang, Yu & Tang, Jiafu & Fung, Richard Y.K., 2014. "A column-generation-based heuristic algorithm for solving operating theater planning problem under stochastic demand and surgery cancellation risk," International Journal of Production Economics, Elsevier, vol. 158(C), pages 28-36.
    3. Fei, Hongying & Meskens, Nadine & Combes, Catherine & Chu, Chengbin, 2009. "The endoscopy scheduling problem: A case study with two specialised operating rooms," International Journal of Production Economics, Elsevier, vol. 120(2), pages 452-462, August.

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