An Efficient Implementation of Geometric Semantic Genetic Programming for Anticoagulation Level Prediction in Pharmacogenetics

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@InProceedings{Castelli:2013:EPIA,
  author =       "Mauro Castelli and Davide Castaldi and 
                 Ilaria Giordani and Sara Silva and Leonardo Vanneschi and 
                 Francesco Archetti and Daniele Maccagnola",
  title =        "An Efficient Implementation of Geometric Semantic
                 Genetic Programming for Anticoagulation Level
                 Prediction in Pharmacogenetics",
  booktitle =    "Proceedings of the 16th Portuguese Conference on
                 Artificial Intelligence, EPIA 2013",
  year =         "2013",
  editor =       "Luis Correia and Luis Paulo Reis and Jose Cascalho",
  volume =       "8154",
  series =       "Lecture Notes in Computer Science",
  pages =        "78--89",
  address =      "Angra do Heroismo, Azores, Portugal",
  month =        sep # " 9-12",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-40668-3",
  URL =          "http://link.springer.com/chapter/10.1007/978-3-642-40669-0_8",
  DOI =          "doi:10.1007/978-3-642-40669-0_8",
  size =         "12 pages",
  abstract =     "The purpose of this study is to develop an innovative
                 system for Coumarin-derived drug dosing, suitable for
                 elderly patients. Recent research highlights that the
                 pharmacological response of the patient is often
                 affected by many exogenous factors other than the
                 dosage prescribed and these factors could form a very
                 complex relationship with the drug dosage. For this
                 reason, new powerful computational tools are needed for
                 approaching this problem. The system we propose is
                 called Geometric Semantic Genetic Programming, and it
                 is based on the use of recently defined geometric
                 semantic genetic operators. In this paper, we present a
                 new implementation of this Genetic Programming system,
                 that allow us to use it for real-life applications in
                 an efficient way, something that was impossible using
                 the original definition. Experimental results show the
                 suitability of the proposed system for managing
                 anticoagulation therapy. In particular, results
                 obtained with Geometric Semantic Genetic Programming
                 are significantly better than the ones produced by
                 standard Genetic Programming both on training and on
                 out-of-sample test data.",
  notes =        "See \cite{Castelli:2013:GECCOcomp}",
}

Genetic Programming entries for Mauro Castelli Davide Castaldi Ilaria Giordani Sara Silva Leonardo Vanneschi Francesco Archetti Daniele Maccagnola

Citations