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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital
verfasst von : Sjoerd van der Spoel, Maurice van Keulen, Chintan Amrit
Erschienen in: Data-Driven Process Discovery and Analysis
Verlag: Springer Berlin Heidelberg
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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.