Behavior patterns in hormonal treatments using fuzzy logic models

Assisted reproductive technologies are a combination of medical strategies designed to treat infertility patients. Ideal stimulation treatment has to be individualized, but one of the main challenges which clinicians face in the everyday clinic is how to select the best medical protocol for a patien...

Descripción completa

Detalles Bibliográficos
Autores: González Enríquez, José, Cid de la Paz, Virginia, Muntaner, N., Aroba Páez, Javier, Navarro, J., Domínguez Mayo, Francisco José, Escalona Cuaresma, María José, Ramos Román, Isabel
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2018
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/88340
Acceso en línea:https://hdl.handle.net/11441/88340
https://doi.org/10.1007/s00500-017-2614-7
Access Level:acceso abierto
Palabra clave:Hormonal treatments
IVF
Fuzzy logic
Data mining
id ES_f5e5ad4263dffb75eb4db1bbd6a05573
oai_identifier_str oai:idus.us.es:11441/88340
network_acronym_str ES
network_name_str España
repository_id_str
spelling Behavior patterns in hormonal treatments using fuzzy logic modelsGonzález Enríquez, JoséCid de la Paz, VirginiaMuntaner, N.Aroba Páez, JavierNavarro, J.Domínguez Mayo, Francisco JoséEscalona Cuaresma, María JoséRamos Román, IsabelHormonal treatmentsIVFFuzzy logicData miningAssisted reproductive technologies are a combination of medical strategies designed to treat infertility patients. Ideal stimulation treatment has to be individualized, but one of the main challenges which clinicians face in the everyday clinic is how to select the best medical protocol for a patient. This work aims to look for behavior patterns in this kind of treatments, using fuzzy logic models with the objective of helping gynecologists and embryologists to make decisions that could improve the process of in vitro fertilization. For this purpose, a real-world dataset composed of one hundred and twenty-three (123) patients and five hundred and fifty-nine (559) treatments applied in relation to such patients provided by an assisted reproduction clinic, has been used to obtain the fuzzy models. As conclusion, this work corroborates some known clinic experiences, provides some new ones and proposes a set of questions to be solved in future experiments.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956- C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-REDTSpringerLenguajes y Sistemas InformáticosTIC021: Ingeniería Web y Testing Temprano2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/88340https://doi.org/10.1007/s00500-017-2614-7reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSoft Computing, 22 (1), 79-90.TIN2013-46928-C3-3-RTIN2016-76956- C3-2-RTIN2015-71938-REDThttps://link.springer.com/article/10.1007/s00500-017-2614-7info:eu-repo/semantics/openAccessoai:idus.us.es:11441/883402026-06-17T12:51:07Z
dc.title.none.fl_str_mv Behavior patterns in hormonal treatments using fuzzy logic models
title Behavior patterns in hormonal treatments using fuzzy logic models
spellingShingle Behavior patterns in hormonal treatments using fuzzy logic models
González Enríquez, José
Hormonal treatments
IVF
Fuzzy logic
Data mining
title_short Behavior patterns in hormonal treatments using fuzzy logic models
title_full Behavior patterns in hormonal treatments using fuzzy logic models
title_fullStr Behavior patterns in hormonal treatments using fuzzy logic models
title_full_unstemmed Behavior patterns in hormonal treatments using fuzzy logic models
title_sort Behavior patterns in hormonal treatments using fuzzy logic models
dc.creator.none.fl_str_mv González Enríquez, José
Cid de la Paz, Virginia
Muntaner, N.
Aroba Páez, Javier
Navarro, J.
Domínguez Mayo, Francisco José
Escalona Cuaresma, María José
Ramos Román, Isabel
author González Enríquez, José
author_facet González Enríquez, José
Cid de la Paz, Virginia
Muntaner, N.
Aroba Páez, Javier
Navarro, J.
Domínguez Mayo, Francisco José
Escalona Cuaresma, María José
Ramos Román, Isabel
author_role author
author2 Cid de la Paz, Virginia
Muntaner, N.
Aroba Páez, Javier
Navarro, J.
Domínguez Mayo, Francisco José
Escalona Cuaresma, María José
Ramos Román, Isabel
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
TIC021: Ingeniería Web y Testing Temprano
dc.subject.none.fl_str_mv Hormonal treatments
IVF
Fuzzy logic
Data mining
topic Hormonal treatments
IVF
Fuzzy logic
Data mining
description Assisted reproductive technologies are a combination of medical strategies designed to treat infertility patients. Ideal stimulation treatment has to be individualized, but one of the main challenges which clinicians face in the everyday clinic is how to select the best medical protocol for a patient. This work aims to look for behavior patterns in this kind of treatments, using fuzzy logic models with the objective of helping gynecologists and embryologists to make decisions that could improve the process of in vitro fertilization. For this purpose, a real-world dataset composed of one hundred and twenty-three (123) patients and five hundred and fifty-nine (559) treatments applied in relation to such patients provided by an assisted reproduction clinic, has been used to obtain the fuzzy models. As conclusion, this work corroborates some known clinic experiences, provides some new ones and proposes a set of questions to be solved in future experiments.
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/88340
https://doi.org/10.1007/s00500-017-2614-7
url https://hdl.handle.net/11441/88340
https://doi.org/10.1007/s00500-017-2614-7
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Soft Computing, 22 (1), 79-90.
TIN2013-46928-C3-3-R
TIN2016-76956- C3-2-R
TIN2015-71938-REDT
https://link.springer.com/article/10.1007/s00500-017-2614-7
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869424658884853760
score 15,301629

Similares en LA Referencia