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Uncover and Assess Rule Adherence Based on Decisions
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2018 (English)In: Lecture Notes in Business Information Processing / [ed] Shishkov B., Springer Verlag , 2018, Vol. 319, p. 249-259Conference paper, Published paper (Refereed)
Abstract [en]

Context: Decisions taken by medical practitioners may be based on explicit and implicit rules. By uncovering these rules, a medical practitioner may have the possibility to explain its decisions in a better way, both to itself and to the person which the decision is affecting. Objective: We investigate if it is possible for a machine learning pipe-line to uncover rules used by medical practitioners, when they decide if a patient could be operated or not. The uncovered rules should have a linguistic meaning. Method: We are evaluating two different algorithms, one of them is developed by us and named “The membership detection algorithm”. The evaluation is done with the help of real-world data provided by a hospital. Results: The membership detection algorithm has significantly better relevance measure, compared to the second algorithm. Conclusion: A machine learning pipe-line, based on our algorithm, makes it possibility to give the medical practitioners an understanding, or to question, how decisions have been taken. With the help of the uncovered fuzzy decision algorithm it is possible to test suggested changes to the feature limits. © Springer International Publishing AG, part of Springer Nature 2018.

Place, publisher, year, edition, pages
Springer Verlag , 2018. Vol. 319, p. 249-259
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348 ; 319
Keywords [en]
Agglomerative merging, Assess rules adherence, Fuzzy decision making, Shannon entropy, Uncovering rules, Artificial intelligence, Decision making, Human computer interaction, Learning systems, Pipelines, Signal detection, Systems engineering, Software design
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-16894DOI: 10.1007/978-3-319-94214-8_16Scopus ID: 2-s2.0-85049679695ISBN: 9783319942131 OAI: oai:DiVA.org:bth-16894DiVA, id: diva2:1239982
Conference
8th International Symposium on Business Modeling and Software Design, BMSD, Vienna
Available from: 2018-08-20 Created: 2018-08-20 Last updated: 2018-08-20Bibliographically approved

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Silvander, JohanSvahnberg, Mikael

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
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Language
  • de-DE
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  • Other locale
More languages
Output format
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