Using a KDD Process to Forecast the Duration of Surgery

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.
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https://hal-ujm.archives-ouvertes.fr/ujm-00331477
Contributor : Catherine Combes <>
Submitted on : Thursday, October 16, 2008 - 6:38:58 PM
Last modification on : Wednesday, July 25, 2018 - 2:05:30 PM

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  • HAL Id : ujm-00331477, version 1

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Catherine Combes, Nadine Meskens, Céline Rivat, Jean-Philippe Va. Using a KDD Process to Forecast the Duration of Surgery. International Journal of Production Economics, Elsevier, 2008, 112 (1), pp.279-293. ⟨ujm-00331477⟩

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