Abstract
Clinical guidelines provide recommendations to assist clinicians in making decisions regarding appropriate medical care for specific patient situations. However, characterizing these situations is difficult as it requires taking into account all the variations that patients may present. We propose an approach which helps with identifying and categorizing the contexts that need to be taken into account within a clinical process. Our methodology is based on a formal process model and on a collection of process execution instances. We apply machine-learning algorithms to group process instances by similarity of their paths and outcomes and derive the contextual properties of each group. We illustrate the application of our methodology to a urinary tract infection management process. Our approach yields promising results with high accuracy for some of the context groups that were identified.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Field, M.J., Lohr, K.N.: Guidelines for Clinical Practice: Directions for a New Program. Institute of Medicine, National Academy Press, Washington (1990)
Ghattas, J., Soffer, P., Peleg, M.: A Goal-based approach for business process learning. In: Workshop on Business Process Modeling, Development, and Support (BPMDS 2008), in conjunction with CAISE 2008, Montpellier, France (2008)
Ploesser, K., Peleg, M., Soffer, P., Rosemann, M., Recker, J.: Learning from Context to Improve Business Processes. In: BPtrends 2009 (1), pp. 1–9 (2009)
Ghattas, J., Soffer, P., Peleg, M.: A formal model for Process context learning. In: 5th workshop on Business process intelligence, Ulm, Germany (2009)
SPSS corporation. SPSS statistics software, version 16 (2009), http://www.spss.com
Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6), 716–723 (1974)
Kass, G.V.: An Exploratory Technique for Investigating Large Quantities of Categorical Data. Journal of Applied Statistics 29(2), 119–127 (1980)
Geisser, S.: Predictive Inference. Chapman and Hall, New York (1993)
Simon, D., Boring, J.R.: Sensitivity, Specificity, and Predictive Value. In: Clinical Methods: The History, Physical, and Laboratory Examinations, 3rd edn. (1990)
Tu, S.W., Campbell, J.R., Glasgow, J., Nyman, M.A., McClure, R.J., McClay, J.P.C., Hrabak, K.M., Berg, D., Weida, T., Mansfield, J.G., Musen, M.A., Abarbanel, R.M.: The SAGE Guideline Model: achievements and overview. J. Am. Med. Inform. Assoc. 14(5), 589–598 (2007)
Russler, D.C., Schadow, G., Mead, C., Snyder, T., Quade, L.M., McDonald, C.J.: Influences of the Unified Service Action Model on the HL7 Reference Information Model. In: Proc. AMIA Symp., pp. 930–934 (1999)
Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of Process Mining in Healthcare - A Case Study in a Dutch Hospital. BIOSTEC 25, 425–438 (2008)
Aamodt, A.E.: EP. Case based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7(1), 39–59 (1994)
Weber, B., Rinderle, S., Wild, W., Reichert, M.: CCBR-Driven Business Process Evolution. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 610–624. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ghattas, J., Peleg, M., Soffer, P., Denekamp, Y. (2010). Learning the Context of a Clinical Process. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds) Business Process Management Workshops. BPM 2009. Lecture Notes in Business Information Processing, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12186-9_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-12186-9_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12185-2
Online ISBN: 978-3-642-12186-9
eBook Packages: Computer ScienceComputer Science (R0)