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Abstract
Predicting the amount of money that can be claimed is critical to the effective running of an Hospital. In this paper we describe a case study of a Dutch Hospital where we use process mining to predict the cash flow of the Hospital. In order to predict the cost of a treatment, we use different data mining techniques to predict the sequence of treatments administered, the duration and the final ”care product” or diagnosis of the patient. While performing the data analysis we encountered three specific kinds of noise that we callsequence noise,human noise andduration noise. Studies in the past have discussed ways to reduce the noise in process data. However, it is not very clear what effect the noise has to different kinds of process analysis. In this paper we describe the combined effect ofsequence noise,human noise andduration noise on the analysis of process data, by comparing the performance of several mining techniques on the data.
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University of Twente, The Netherlands
Sjoerd van der Spoel, Maurice van Keulen & Chintan Amrit
- Sjoerd van der Spoel
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- Maurice van Keulen
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- Chintan Amrit
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Editors and Affiliations
University of Fribourg, Switzerland
Philippe Cudre-Mauroux
Università degli Studi di Milano, Italy
Paolo Ceravolo
Athabasca University, AB, Canada
Dragan Gašević
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van der Spoel, S., van Keulen, M., Amrit, C. (2013). Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital. In: Cudre-Mauroux, P., Ceravolo, P., Gašević, D. (eds) Data-Driven Process Discovery and Analysis. SIMPDA 2012. Lecture Notes in Business Information Processing, vol 162. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40919-6_4
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