TY - GEN
T1 - An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics
AU - Castelli, Mauro
AU - Castaldi, Davide
AU - Giordani, Ilaria
AU - Silva, Sara
AU - Vanneschi, Leonardo
AU - Archetti, Francesco
AU - Maccagnola, Daniele
N1 - Castelli, M., Castaldi, D., Giordani, I., Silva, S., Vanneschi, L., Archetti, F., & Maccagnola, D. (2013). An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics. In Progress in Artificial Intelligence - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Proceedings (pp. 78-89). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8154 LNAI). https://doi.org/10.1007/978-3-642-40669-0_8
PY - 2013/10/3
Y1 - 2013/10/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84884729122&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40669-0_8
DO - 10.1007/978-3-642-40669-0_8
M3 - Conference contribution
AN - SCOPUS:84884729122
SN - 9783642406683
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 89
BT - Progress in Artificial Intelligence - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Proceedings
T2 - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013
Y2 - 9 September 2013 through 12 September 2013
ER -