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. © 2013 Springer-Verlag.

Castelli, M., Castaldi, D., Giordani, I., DIAS PEREIRA SA DA SILVA, S., Vanneschi, L., Archetti, F., et al. (2013). An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics. In L. Correia, L.P. Reis, J. Cascalho (a cura di), Progress in Artificial Intelligence, EPIA 2013 (pp. 78-89). Springer-Verlag [10.1007/978-3-642-40669-0_8].

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

CASTELLI, MAURO
Primo
;
CASTALDI, DAVIDE FABIO
Secondo
;
GIORDANI, ILARIA;DIAS PEREIRA SA DA SILVA, SARA;VANNESCHI, LEONARDO;ARCHETTI, FRANCESCO ANTONIO
Penultimo
;
MACCAGNOLA, DANIELE
Ultimo
2013

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. © 2013 Springer-Verlag.
Capitolo o saggio
Geometric Semantic Genetic Programming, Oral anticoagulation, Pharmacogenetics
English
Progress in Artificial Intelligence, EPIA 2013
978-3-642-40668-3
Castelli, M., Castaldi, D., Giordani, I., DIAS PEREIRA SA DA SILVA, S., Vanneschi, L., Archetti, F., et al. (2013). An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics. In L. Correia, L.P. Reis, J. Cascalho (a cura di), Progress in Artificial Intelligence, EPIA 2013 (pp. 78-89). Springer-Verlag [10.1007/978-3-642-40669-0_8].
Castelli, M; Castaldi, D; Giordani, I; DIAS PEREIRA SA DA SILVA, S; Vanneschi, L; Archetti, F; Maccagnola, D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/59109
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