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From Data and Models to Decision Support Systems: Lessons and Advice for the Future

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EURO Working Group on DSS

Part of the book series: Integrated Series in Information Systems ((ISIS))

Abstract

Model-based Decision Support Systems (DSSs) employ various types of models, such as statistical, optimization, simulation, or rule-based. Models are used to assess and analyze the given decision situation, and on this basis advise the decision-maker. Generally, the DSS development process involves three steps: (1) model development, (2) implementing the model(s) in a DSS, and (3) using the DSS. In this chapter, we focus on two model development approaches: Data Mining and Expert Modeling. We advocate for combing the two in order to get better models and better DSSs in general. We illustrate some points and potential pitfalls using an example of the PD_manager DSS, which is aimed at supporting medication change decisions in the management of Parkinson’s disease. Based on the experience from PD_manager and some other DSS development projects, we propose the so-called 5C requirements for better DSS models: correctness, completeness, consistency, comprehensibility, and convenience. Finally, we summarize the lessons learned and give advice to DSS developers and researchers.

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Notes

  1. 1.

    Please see http://kt.ijs.si/MarkoBohanec/mare.html for more information about these projects.

  2. 2.

    See, for example, https://www.softwaretestinghelp.com/data-mining-tools/.

  3. 3.

    https://www.capterra.com/decision-support-software/, http://kt.ijs.si/MarkoBohanec/dss.html, https://www.mcdmsociety.org/content/software-related-mcdm.

  4. 4.

    https://www.decision-deck.org/project/.

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Acknowledgments

The author acknowledges the financial support from the Slovenian Research Agency, research core funding P2-0103. The PD_manager project was funded within the EU Framework Programme for Research and Innovation Horizon 2020, under grant number 643706. Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI—a public–private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Allergan, Amathus, Avid, Biogen, BioLegend, Bristol-Myers Squibb, Celgene, Jenali, GE Healthcare, Genentech, GlaxoSmithKline, Janssen Neuroscience, Lilly, Lundbeck, Merck, MSD, Pfizer, Piramal, Prevail, Roche, Sanofy Genzyme, Servier, Takeda, Teva, UCB, Verily, and Voyager.

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Authors and Affiliations

  1. Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia

    Marko Bohanec

  2. University of Nova, Gorica, Slovenia

    Marko Bohanec

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  1. Marko Bohanec
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Correspondence to Marko Bohanec .

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Editors and Affiliations

  1. Department of Business Administration, University of Macedonia, Thessaloniki, Greece

    Jason Papathanasiou

  2. University Toulouse 1 Capitole - IRIT, Toulouse University, Toulouse, France

    Pascale Zaraté

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    Jorge Freire de Sousa

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Bohanec, M. (2021). From Data and Models to Decision Support Systems: Lessons and Advice for the Future. In: Papathanasiou, J., Zaraté, P., Freire de Sousa, J. (eds) EURO Working Group on DSS. Integrated Series in Information Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-70377-6_11

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