Supporting Medical Decisions for Treating Rare Diseases Through Genetic Programming

  • Illya BakurovEmail author
  • Mauro Castelli
  • Leonardo Vanneschi
  • Maria João Freitas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11454)


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.


Genetic Programming Geometric Semantic Genetic Programming Medical decisions Rare diseases 



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).


  1. 1.
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Centro de desenvolvimento e reabilitação da casa dos marcos.
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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). Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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). Scholar
  11. 11.
    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). Scholar
  12. 12.
    Smith, S.L., Cagnoni, S.: Genetic and Evolutionary Computation: Medical Applications. Wiley, Chichester (2011)Google Scholar
  13. 13.
    Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  14. 14.
    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). Scholar
  15. 15.
    Vanneschi, L., Castelli, M., Silva, S.: A survey of semantic methods in genetic programming. Genet. Program Evolvable Mach. 15(2), 195–214 (2014)CrossRefGoogle Scholar
  16. 16.
    Castelli, M., Silva, S., Vanneschi, L.: A c++ framework for geometric semantic genetic programming. Genet. Program Evolvable Mach. 16(1), 73–81 (2015)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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). Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    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). Scholar
  22. 22.
    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). Scholar
  23. 23.
    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)Google Scholar
  24. 24.
    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). Scholar

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Authors and Affiliations

  1. 1.Nova Information Management School (NOVA IMS)Universidade Nova de LisboaLisbonPortugal
  2. 2.Raríssimas - Associação Nacional de Deficiências Mentais e RarasLisbonPortugal

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