An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics

Mauro Castelli, Davide Castaldi, Ilaria Giordani, Sara Silva, Leonardo Vanneschi, Francesco Archetti, Daniele Maccagnola

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Proceedings
Pages78-89
Number of pages12
DOIs
Publication statusPublished - 3 Oct 2013
Event16th Portuguese Conference on Artificial Intelligence, EPIA 2013 - Angra do Heroismo, Azores, Portugal
Duration: 9 Sep 201312 Sep 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8154 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Portuguese Conference on Artificial Intelligence, EPIA 2013
CountryPortugal
CityAngra do Heroismo, Azores
Period9/09/1312/09/13

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