Explainer: An interactive Agent for Explaining the Diagnosis of Cardiac Arrhythmia Generated by IK-DCBRC
DOI:
https://doi.org/10.26415/2572-004X-vol3iss2p376-394Keywords:
Case-Based reasoning, explanation-aware computing, Cardiac arrhythmias, intelligent agent.Abstract
Interactions between medical applications and users involve a high level of trust, since many complex, automated applications are integrated and involve critical domains in which public health is paramount. Although uncertainty decreases the accuracy and trust of such medical applications under these circumstances, explanation-aware computing becomes crucial in improving the efficiency of these applications. This paper describes an intelligent agent that interacts with users to provide meaningful explanations of previous diagnoses supported by IK-DCBRC. The agent ensures intelligent interactions with users via a rule-based system that generates appropriate explanations according to the selected level of abstraction and the detected cardiac arrhythmia. The paper also describes a particular medical application, that is, cardiac arrhythmia with automatic diagnoses supported by the case-based reasoning classifier, IK-DCBRC.
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References
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2. Khelassi, A., Yelles-chaouche, S. N., & Benais, F. (2017). Multi-arrhythmias detection with an XML rule-based system from 12-Lead Electrocardiogram. Electronic physician, 9(5), 4357.
3. Abdeldjalil KHELASSI, Mohamed Amin Chick. Fuzzy knowledge-intensive case based classification for the detection of abnormal cardiac beats. Electronic Physician, 2012;4(3):565-571.
4. Smisek, R., Hejc, J., Ronzhina, M., Nemcova, A., Marsanova, L., Kolarova, J., ... & Vitek, M. (2018). Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device. Physiological Measurement.
5. Xiong, Z., Nash, M. P., Cheng, E., Fedorov, V. V., Stiles, M. K., & Zhao, J. (2018). ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiological Measurement.
6. Klir, G.J. and B. Yuan. (1995). Fuzzy Sets and Fuzzy Logic. Theory. and Applications, Ed. Prentice-Hall.
7. Aamodt, Agnar, and Enric Plaza. "Case-based reasoning: Foundational issues, methodological variations, and system approaches." AI communications 7.1 (1994): 39-59.
8. Hadjiski, M., Grancharova, A., & Boshnakov, K. (2018). Intelligent Two-Level Optimization and Model Predictive Control of Degrading Plants. In Learning Systems: From Theory to Practice (pp. 117-134). Springer, Cham.
9. Frode Sørmo, Jörg Cassens, and Agnar Aamodt “Explanation in Case-Based Reasoning –Perspectives and Goals” Artificial Intelligence Review, 24(2):109–143, October 2005.
10. John R. Hampton, 2013, The ECG Made Easy, Churchill Livingstone; 8 edition (August 27, 2013). ISBN-10: 0702046418.
11. Acharya, U. R., Hagiwara, Y., Koh, J. E. W., Oh, S. L., Tan, J. H., Adam, M., & San Tan, R. (2018). Entropies for automated detection of coronary artery disease using ECG signals: A review. Biocybernetics and Biomedical Engineering.
12. Pan J, Tompkins WJ, A real-time QRS detection algorithm, IEEE Trans Biomed Eng 32:230–236, 1985.
13. Berwal, D., Kumar, A., & Kumar, Y. (2018). Design of high performance QRS complex detector for wearable healthcare devices using biorthogonal spline wavelet transform. ISA transactions.
14. Sotelo, S., Arenas, W., & Altuve, M. (2018, April). QRS complex detection based on continuous density hidden Markov models using univariate observations. In Journal of Physics: Conference Series (Vol. 1002, No. 1, p. 012009). IOP Publishing.
15. Ouali, M. A., Ghanai, M., & Chafaa, K. (2018). A new type-2 fuzzy modelling and identification for electrophysiological signals: a comparison between PSO, BBO, FA and GA approaches. International Journal of Modelling, Identification and Control, 29(2), 163-184.
16. Rai, H. M., & Chatterjee, K. (2018). A Novel Adaptive Feature Extraction for Detection of Cardiac Arrhythmias Using Hybrid Technique MRDWT & MPNN Classifier from ECG Big Data. Big data research.
17. Zhaohao Sun, Jun Han, and Dong Dong (2008). Five Perspectives on Case Based Reasoning. In: D.-S. Huang et al. (Eds.): ICIC 2008, LNAI 5227, pp. 410-419, 2008.
18. Plaza, Enric; McGinty, Lorraine (2006). Distributed case-based reasoning.The Knowledge engineering review, Vol. 20, N. 3, pp. 261-265.
19. González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., & Corchado, J. M. (2018). Energy optimization using a case-based reasoning strategy. Sensors, 18(3), 865.
20. Miller, K., & Mansingh, G. (2017). OptiPres: a distributed mobile agent decision support system for optimal patient drug prescription. Information Systems Frontiers, 19(1), 129-148.
21. Cho, B., Kim, K. J., & Chung, J. W. (2017). CBR-based network performance management with multi-agent approach. Cluster Computing, 20(1), 757-767.
22. Aissani, N., Guetarni, I. H. M., & Zebirate, S. (2017). Dynamic control for safety system multi-agent system with case-based reasoning. International Journal of Reliability and Safety, 11(3-4), 238-255.
23. A. Khelassi (2012). ''Distributed Case Based Reasoning for Cardiac Arrhythmias: Health sciences application'' LAMBERT ACADEMIC PUBLISHING -LAP-.
24. A.khelassi: ‘’Data mining application with case based reasoning classifier for breast cancer decision support’’. MASAUM Inter Conf on Info Tech 2012 MI-CIT’12; 07/2012
25. A.Khelassi, “Fuzzy Case Based Classification applied in the classification of breast cancer pattern” Doctorial STIC11, TEBASSA, Algeria, 2011.
26. Khelassi, MA Chikh “Fuzzy knowledge-intensive case based classification applied in the automatic cardiac arrhythmias diagnosis” Colloque sur L’Optimisation et les Systèmes d’Information COSI 2011 P460, 24 -27 Avril, Guelma, Algérie
27. A.KHELASSI “Fuzzy Similarity Based classification Versus Intensive Knowledge Case Based classification” Colloque International sur les Nouvelles Techniques Immuno-Cognitive dans les Réseaux Informatiques NTICRI ’09, Oran, Algeria
28. A.KHELASSI, M A CHIKH «Distributed classification using Intensive Knowledge Case Based Reasoning'' Conférence Internationale des Technologies de l’Information et de la Communication CITIC’2009. Setif Algeria.
29. Abdeldjalil Khelassi, Mohamed Amin Chikh: Cognitive amalgam with a distributed fuzzy case-based reasoning system for an accurate cardiac arrhythmias diagnosis. Int. J. Information and Communication Technology IJICT. 07/2015; 7(4/5):348-365.
30. A.KHELASSI, M A CHIKH " Distributed case based reasoning for cardiac arrhythmias diagnosis'' MCSEAI 2008 Maghrebian Conference of Software Engineering and Artificial Intelligence, 28- 30 April 2008, Oran, Algérie,
31. Khelassi, A. (2015, January). The impact of uncertainty measures in the cardiac arrhythmia detection supported by IK-DCBRC. In Proceedings of the International Conference on Information and Knowledge Engineering (IKE) (p. 144). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
32. KHELASSI, Abdeldjalil. Explanation-aware computing of the prognosis for breast cancer supported by IK-DCBRC: Technical innovation. Electronic Physician, 2014, vol. 6, no 4, p. 947.
33. Agnar Aamodt. A Knowledge-Intensive, Integrated Approach to Problem Solving and Sustained Learning. PhD thesis, Norwegian Institute of Technology, Department of Computer Science, Trondheim, May 1991.
34. Thomas R. Roth-Berghofer and Jörg Cassens “Mapping Goals and Kinds of Explanations to the Knowledge Containers of Case-Based Reasoning Systems”, ICCBR 2005, volume 3630 of LNAI, pages 451– 464, Chicago, 2005. Springer.
35. William J. Clancey. The epistemology of a rule-based expert system: A framework for explanation. Artificial Intelligence, 20(3):215–251, 1983.
36. William R. Swartout. What kind of expert should a system explain: A system for creating and explaining expert consulting programs. Artificial Intelligence, 21:285–325, 1983.
37. William R. Swartout and Stephen W. Smoliar. On making expert systems more like experts. Expert Systems, 4(3):196–207, 1987.
38. Goh, Y. M., & Guo, B. H. (2018). FPSWizard: A web-based CBR-RBR system for supporting the design of active fall protection systems. Automation in Construction, 85, 40-50.
39. LIND, Linnéa Karlsson, VON EULER, Mia, KORKMAZ, Seher, et al. Sex differences in drugs: the development of a comprehensive knowledge base to improve gender awareness prescribing. Biology of sex differences, 2017, vol. 8, no 1, p. 32.
40. Khelassi, A. (2016, January). An augmented pragmatics by explanation-aware and recommendation-aware in the context of decision support. In Proceedings of the International Conference on Information and Knowledge Engineering (IKE) (p. 79). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Published
2019-07-13
Issue
Section
Medical technologies
