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Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital

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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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Author information

Authors and Affiliations

  1. University of Twente, The Netherlands

    Sjoerd van der Spoel, Maurice van Keulen & Chintan Amrit

Authors
  1. Sjoerd van der Spoel

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  2. Maurice van Keulen

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  3. Chintan Amrit

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Editor information

Editors and Affiliations

  1. University of Fribourg, Switzerland

    Philippe Cudre-Mauroux

  2. Università degli Studi di Milano, Italy

    Paolo Ceravolo

  3. Athabasca University, AB, Canada

    Dragan Gašević

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© 2013 IFIP International Federation for Information Processing

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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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