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

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 call sequence noise, human noise and duration 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 of sequence noise, human noise and duration noise on the analysis of process data, by comparing the performance of several mining techniques on the data.
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Sjoerd Spoel, Maurice Keulen, Chintan Amrit. Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital. 2nd International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Jun 2012, Campione d’Italia, Italy. pp.60-83, ⟨10.1007/978-3-642-40919-6_4⟩. ⟨hal-01474690⟩

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