Abstract
An initial case base population naturally lacks diversity of solutions. In order to overcome this cold-start problem, we present how genetic algorithms (GA) can be applied. The work presented in this paper is part of the selfBACK EU project and describes a case-based recommendation system that creates exercise plans for patients with non-specific low back pain (LBP). In selfBACK Case-Based Reasoning (CBR) is used as its main methodology for generating patient-specific advice for managing non-specific LBP. The sub-module of selfBACK presented in this work focuses on the adaptation process of exercise plans: A GA inspired method is created to increase the variation of personalized exercise plans, which today are crafted by medical professionals. Experiments are conducted using real patients’ characteristics with expert-crafted solutions and automatically generated solutions. In the evaluation we compare the quality of the GA-generated solutions to null-adaptation solutions.
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References
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. Artif. Intell. Commun. 7(1), 39–59 (1994)
Bach, K., Szczepanski, T., Aamodt, A., Gundersen, O.E., Mork, P.J.: Case representation and similarity assessment in the selfBACK decision support system. ICCBR 2116 (accepted for publication)
Bareiss, R.: Exemplar-based knowledge acquisition (1989)
Begum, S., Ahmed, M.U., Xiong, N., Folke, M.: Case based reasoning systems in the health sciences a survey of recent trends and developments. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41, 421–434 (2010)
Bichindaritz, I.: Case-based reasoning in the health sciences: why it matters for the health sciences and for CBR. In: Althoff, K.D., Bergmann, R., Minor, M., Hanft, A. (eds.) Advances in Case-Based Reasoning. LNCS, vol. 5239, pp. 1–17. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85502-6_1
Brox, J.: Ryggsmerter. In: Aktivitetshåndboken - Fysisk aktivitet i forebygging og behandling, pp. 537–547. Helsedirektoratet (2009)
da Costa, LCM., Maher, C.G., McAuley, J.H., Hancock, M.J., Herbert, R.D., Refshauge, K.M., Henschke, N.: Prognosis for patients with chronic low back pain: inception cohort study (2009)
Chang, C., Cui, J., Wang, D., Hu, K.: Research on case adaptation techniques in case-based reasoning. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics. IEEE (2004)
Choudhury, N.: A survey on case-based reasoning in medicine. Int. J. Adv. Comput. Sci. Appl. 7(8), 136–144 (2016)
Deyo, R.A., Battie, M., Beurskens, A., Bombardier, C., Croft, P., Koes, B., Malmivaara, A., Roland, M., Von Korff, M., Waddell, G.: Outcome measures for low back pain research. A proposal for standardized use 23(18), 2003–2013 (1998)
de A, G., Maher, M.L.: An evolutionary approach to case adaptation. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS, vol. 1650, pp. 162–173. Springer, Heidelberg (1999). doi:10.1007/3-540-48508-2_12
Grech, A., Main, J.: Case-base injection schemes to case adaptation using genetic algorithms. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 198–210. Springer, Heidelberg (2004). doi:10.1007/978-3-540-28631-8_16
Husain, W., Wei, L.J., Cheng, S.L., Zakaria, N.: Application of data mining techniques in a personalized diet recommendation system for cancer patients. In: IEEE Colloquium on Humanities, Science and Engineering. IEEE Xplore (2011)
Ben Schafer, J., Dan Frankowski, J.: Collaborative filtering recommender systems (2007)
Fritz, J.M., George, S.Z., Delitto, A.: The role of fear-avoidance beliefs in acute low back pain: relationships with current and future disability and work status. Pain 94(1), 7–15 (2001)
Kofod-Petersen, A., Cassens, J., Aamodt, A.: Explanatory capabilities in the creek knowledge-intensive case-based reasoner. In: Proceedings of the 2008 Conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008, pp. 28–35. IOS Press, Amsterdam, The Netherlands (2008)
Koton, P.: Reasoning about evidence in causal explanations. In: Proceedings of the Seventh AAAI National Conference on Artificial Intelligence, AAAI 1988, Saint Paul, Minnesota, pp. 256–261. AAAI Press (1988). http://dl.acm.org/citation.cfm?id=2887965.2888011
Lærum, E., Brox, J.I., Storheim, K., Espeland, A., Haldorsen, E., Munch-Ellingsen, J., Nielsen, L., Rossvoll, I., Skouen, J.S., Stig, L., Werner, E.L.: Nasjonale kliniske retningslinjene for korsryggsmerter. Formi (2007)
Marling, C., Rissland, E., Aamodt, A.: Integrations with case-based reasoning. Knowl. Eng. Rev. 20(3), 241–245 (2005)
Nikpour, H., Aamodt, A., Skalle, P.: Diagnosing root causes and generating graphical explanations by integrating temporal causal reasoning and CBR. In: Coman, A., Kapetanakis, S. (eds.) Workshops Proceedings for the Twenty-Fourth International Conferenceon Case-Based Reasoning (ICCBR 2016), Atlanta, Georgia, USA, 31 October–2 November 2016. CEUR Workshop Proceedings, vol. 1815, pp. 162–172. CEUR-WS.org (2016)
Petrovic, S., Khussainova, G., Jagannathan, R.: Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning. Artif. Intell. Med. 68, 17–28 (2016). ScienceDirect
Schmidt, R., Montani, S., Bellazzi, R., Portinale, L., Gierl, L.: Cased-based reasoning for medical knowledge-based systems. Int. J. Med. Inf. 64, 355–367 (2001). ScienceDirect
Senanayke, S., Malik, O.A., Iskandar, P.M., Zaheer, D.: A hybrid intelligent system for recovery and performance evaluation after anterior cruciate ligament injury. In: 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE (2012)
Spears, V.M., Jong, K.A.D.: On the virtues of parameterized uniform crossover. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 230–236 (1991)
Acknowledgement
The work has been conducted as part of the selfBACK project, which has received funding from the European Union’s Horizon 2020 research and innovation programmer under grant agreement No. 689043.
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Prestmo, T., Bach, K., Aamodt, A., Mork, P.J. (2017). Evolutionary Inspired Adaptation of Exercise Plans for Increasing Solution Variety. In: Aha, D., Lieber, J. (eds) Case-Based Reasoning Research and Development. ICCBR 2017. Lecture Notes in Computer Science(), vol 10339. Springer, Cham. https://doi.org/10.1007/978-3-319-61030-6_19
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