Abstract
Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centers like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rare disease resources & FAQs. https://rarediseases.org/for-patients-and-families/information-resources/resources-faqs/
Scheeren, E.M., Mascarenhas, L.P.G., Chiarello, C.R., Costin, A.C.M.S., Oliveira, L., Neves, E.B.: Description of the pediasuit protocol\(^{TM}\). Fisioterapia em movimento 25(3), 473–480 (2012)
Centro de desenvolvimento e reabilitação da casa dos marcos. http://rarissimas.pt/centro-de-desenvolvimento-e-reabilitacao-da-casa-dos-marcos/
Russell, D.J., Rosenbaum, P.L., Cadman, D.T., Gowland, C., Hardy, S., Jarvis, S.: The gross motor function measure: a means to evaluate the effects of physical therapy. Dev. Med. Child Neurol. 31(3), 341–352 (1989)
Bojarczuk, C.C., Lopes, H.S., Freitas, A.A., Michalkiewicz, E.L.: A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. Artif. Intell. Med. 30(1), 27–48 (2004)
Castelli, M., Vanneschi, L., Manzoni, L., Popovič, A.: Semantic genetic programming for fast and accurate data knowledge discovery. Swarm Evol. Comput. 26, 1–7 (2016)
Hu, T., Oksanen, K., Zhang, W., Randell, E., Furey, A., Zhai, G.: Analyzing feature importance for metabolomics using genetic programming. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds.) EuroGP 2018. LNCS, vol. 10781, pp. 68–83. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77553-1_5
Beger, R.D., et al.: For “Precision Medicine and Pharmacometabolomics Task Group”-metabolomics society initiative: metabolomics enables precision medicine: “a white paper, community perspective”. Metabolomics 12(9), 149 (2016)
Castelli, M., Vanneschi, L., Popovič, A.: Parameter evaluation of geometric semantic genetic programming in pharmacokinetics. Int. J. Bio-Inspired Comput. 8(1), 42–50 (2016)
Castelli, M., et al.: An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics. In: Correia, L., Reis, L.P., Cascalho, J. (eds.) EPIA 2013. LNCS (LNAI), vol. 8154, pp. 78–89. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40669-0_8
Vanneschi, L., Castelli, M., Manzoni, L., Silva, S.: A new implementation of geometric semantic GP and its application to problems in pharmacokinetics. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 205–216. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37207-0_18
Smith, S.L., Cagnoni, S.: Genetic and Evolutionary Computation: Medical Applications. Wiley, Chichester (2011)
Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32937-1_3
Vanneschi, L., Castelli, M., Silva, S.: A survey of semantic methods in genetic programming. Genet. Program Evolvable Mach. 15(2), 195–214 (2014)
Castelli, M., Silva, S., Vanneschi, L.: A c++ framework for geometric semantic genetic programming. Genet. Program Evolvable Mach. 16(1), 73–81 (2015)
Castelli, M., Manzoni, L., Gonçalves, I., Vanneschi, L., Trujillo, L., Silva, S.: An analysis of geometric semantic crossover: a computational geometry approach. In: IJCCI (ECTA), pp. 201–208 (2016)
Oliveira, L.O.V., Otero, F.E., Pappa, G.L.: A dispersion operator for geometric semantic genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 773–780. ACM (2016)
Pawlak, T.P., Krawiec, K.: Semantic geometric initialization. In: Heywood, M.I., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds.) EuroGP 2016. LNCS, vol. 9594, pp. 261–277. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30668-1_17
Vanneschi, L., Bakurov, I., Castelli, M.: An initialization technique for geometric semantic GP based on demes evolution and despeciation. In: IEEE Congress on Evolutionary Computation (CEC), pp. 113–120. IEEE (2017)
Bakurov, I., Vanneschi, L., Castelli, M., Fontanella, F.: EDDA-V2 – an improvement of the evolutionary demes despeciation algorithm. In: Auger, A., Fonseca, C.M., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds.) PPSN 2018. LNCS, vol. 11101, pp. 185–196. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99253-2_15
Bartashevich, P., Bakurov, I., Mostaghim, S., Vanneschi, L.: PSO-based search rules for aerial swarms against unexplored vector fields via genetic programming. In: Auger, A., Fonseca, C.M., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds.) PPSN 2018. LNCS, vol. 11101, pp. 41–53. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99253-2_4
Bartashevich, P., Bakurov, I., Mostaghim, S., Vanneschi, L.: Evolving PSO algorithm design in vector fields using geometric semantic GP. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, 15–19 July 2018, pp. 262–263 (2018)
Vanneschi, L., Silva, S., Castelli, M., Manzoni, L.: Geometric semantic genetic programming for real life applications. In: Riolo, R., Moore, J.H., Kotanchek, M. (eds.) Genetic Programming Theory and Practice XI. GEC, pp. 191–209. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0375-7_11
Acknowledgments
This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project DSAIPA/DS/0022/2018 (GADgET) and project PTDC/CCI-INF/29168/2017 (BINDER).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bakurov, I., Castelli, M., Vanneschi, L., Freitas, M.J. (2019). Supporting Medical Decisions for Treating Rare Diseases Through Genetic Programming. In: Kaufmann, P., Castillo, P. (eds) Applications of Evolutionary Computation. EvoApplications 2019. Lecture Notes in Computer Science(), vol 11454. Springer, Cham. https://doi.org/10.1007/978-3-030-16692-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-16692-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16691-5
Online ISBN: 978-3-030-16692-2
eBook Packages: Computer ScienceComputer Science (R0)