Abstract
In spite the important advantages of influence diagrams over decision trees, including the possibility of solving much more complex problems, the medical literature still contains around 10 decision trees for each influence diagram. In this paper we analyse the reasons for the low acceptance of influence diagrams in health decision analysis, in contrast with its success in artificial intelligence. One of the reasons is the difficulty of representing asymmetric problems. Another one was the lack of algorithms for explaining the reasoning and performing cost-effectiveness analysis, as well as the scarcity of user-friendly software tools for sensitivity analysis. In this paper we review the research conducted by our group in the last 25 years, crystallised in the open-source software tool OpenMarkov, explaining how it has tried to address those challenges.
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Acknowledgements
This work has been supported by the Spanish Government under grants TIN2009-09158, PI13/02446, and TIN2016-77206-R, and co-financed by the European Regional Development Fund (ERDF). It has also received support from projects 262266 and 324401 (FP7-PEOPLE-2012-IAPP) of the European Union.
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Arias, M., Artaso, M.Á., Bermejo, I., Díez, F.J., Luque, M., Pérez-Martín, J. (2017). Advanced Algorithms for Medical Decision Analysis. Implementation in OpenMarkov. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_43
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DOI: https://doi.org/10.1007/978-3-319-59758-4_43
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