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Uncover and Assess Rule Adherence Based on Decisions
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-0396-1993
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_16ISI: 000465515000016Scopus ID: 2-s2.0-85049679695ISBN: 9783319942131 (print)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: 2021-10-15Bibliographically approved
In thesis
1. Towards Intent-Driven Systems Based on Context Frames
Open this publication in new window or tab >>Towards Intent-Driven Systems Based on Context Frames
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this research project we investigate how machine actors can support the business intents (desired outcomes) of an enterprise via predictive execution flows, prescriptive execution flows, and bidirectional knowledge creation between human actors and machine actors. A context frame supports bidirectional knowledge creation via interventions and counterfactual analysis. An intent-driven system combines execution flows to obtain business intents, and a context frame is a component in these flows. 

Our aim is to develop theoretical frameworks supporting intent-driven systems and context frames, and to validate the components needed to realize such frame- works. 

We are using the design science framework as our research framework. During our design science study we have used the following research methods: systematic literature review, case study, quasi experiment, action research, and evaluation research. 

We have created theoretical frameworks supporting intent-driven systems, and context frames, and implemented needed functionality in the involved components. The framework supports knowledge creation and knowledge validation. The possibility of using the knowledge for predictive analysis, prescriptive analysis, and counterfactual analysis, makes it possible to obtain bidirectional knowledge creation between a human actor and a machine actor. This enables a context frame to be part of an intent-driven system which supports predictive, and prescriptive, executions flows. 

The produced artifacts provide answers to our research questions. These answers are a base for theoretical frameworks supporting intent-driven systems and context frames, and provide knowledge of how to construct the components needed to realize these frameworks. 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2021. p. 50
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2021:08
Keywords
business intent, knowledge creation, decision making, rule adherence, OODA-loop, causal models
National Category
Software Engineering
Research subject
Software Engineering
Identifiers
urn:nbn:se:bth-22178 (URN)978-91-7295-430-4 (ISBN)
Public defence
2021-12-10, J1630 + Zoom, Campus Gräsvik, Karlskrona, 09:30 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2021-10-08 Created: 2021-10-07 Last updated: 2021-11-23Bibliographically approved

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Silvander, JohanSvahnberg, Mikael

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