Learning target-based preferences through additive models: An application in radiotherapy treatment planning
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
DOI: | 10.1016/j.ejor.2021.12.011 |
Texto Completo: | http://hdl.handle.net/10316/97198 https://doi.org/10.1016/j.ejor.2021.12.011 |
Resumo: | This article presents a new Multi-Criteria Decision Aiding preference disaggregation method based on an asymmetric target-based model. The decision maker’s preferences are elicited considering the choices made given a set of comparisons among pairs of solutions (the stimuli). It is assumed that the decision maker has a reference value (target) for the stimulus. Solutions that do not comply with this reference value for some of the criteria dimensions considered will be penalized, and an inferred weight is as- sociated with each dimension to calculate a penalty score for each solution. One of the differentiating features of the proposed model when compared with other existing models is the fact that only solu- tions that do not meet the target are penalized. The target is not interpreted as an ideal solution, but as a set of threshold values that should be taken into account when choosing a solution. The proposed ap- proach was applied to the problem of choosing radiotherapy treatment plans, using a set of retrospective cancer cases treated at the Portuguese Oncology Institute of Coimbra. Using paired comparison choices made by one radiation oncologist, the preference model was built and was tested with in-sample and out-of-sample data. It is possible to conclude that the preference model is capable of representing the radiation oncologist’s preferences, presenting small mean errors and leading, most of the time, to the same treatment plan chosen by the radiation oncologist. |
id |
RCAP_06d9f4c5eeccf8bee8216b407ecf5a92 |
---|---|
oai_identifier_str |
oai:estudogeral.uc.pt:10316/97198 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Learning target-based preferences through additive models: An application in radiotherapy treatment planningMultiple criteria analysisOR in HealthPreference disaggregationRadiotherapyThis article presents a new Multi-Criteria Decision Aiding preference disaggregation method based on an asymmetric target-based model. The decision maker’s preferences are elicited considering the choices made given a set of comparisons among pairs of solutions (the stimuli). It is assumed that the decision maker has a reference value (target) for the stimulus. Solutions that do not comply with this reference value for some of the criteria dimensions considered will be penalized, and an inferred weight is as- sociated with each dimension to calculate a penalty score for each solution. One of the differentiating features of the proposed model when compared with other existing models is the fact that only solu- tions that do not meet the target are penalized. The target is not interpreted as an ideal solution, but as a set of threshold values that should be taken into account when choosing a solution. The proposed ap- proach was applied to the problem of choosing radiotherapy treatment plans, using a set of retrospective cancer cases treated at the Portuguese Oncology Institute of Coimbra. Using paired comparison choices made by one radiation oncologist, the preference model was built and was tested with in-sample and out-of-sample data. It is possible to conclude that the preference model is capable of representing the radiation oncologist’s preferences, presenting small mean errors and leading, most of the time, to the same treatment plan chosen by the radiation oncologist.Elsevier2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/97198http://hdl.handle.net/10316/97198https://doi.org/10.1016/j.ejor.2021.12.011eng03772217https://www.sciencedirect.com/science/article/pii/S0377221721010183Dias, Luis C.Dias, JoanaVentura, TiagoRocha, HumbertoFerreira, Brígida da CostaKhouri, LeilaLopes, Maria do Carmo Carrilho Calado Antunesinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-05-25T07:00:46Zoai:estudogeral.uc.pt:10316/97198Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:15:17.332285Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Learning target-based preferences through additive models: An application in radiotherapy treatment planning |
title |
Learning target-based preferences through additive models: An application in radiotherapy treatment planning |
spellingShingle |
Learning target-based preferences through additive models: An application in radiotherapy treatment planning Learning target-based preferences through additive models: An application in radiotherapy treatment planning Dias, Luis C. Multiple criteria analysis OR in Health Preference disaggregation Radiotherapy Dias, Luis C. Multiple criteria analysis OR in Health Preference disaggregation Radiotherapy |
title_short |
Learning target-based preferences through additive models: An application in radiotherapy treatment planning |
title_full |
Learning target-based preferences through additive models: An application in radiotherapy treatment planning |
title_fullStr |
Learning target-based preferences through additive models: An application in radiotherapy treatment planning Learning target-based preferences through additive models: An application in radiotherapy treatment planning |
title_full_unstemmed |
Learning target-based preferences through additive models: An application in radiotherapy treatment planning Learning target-based preferences through additive models: An application in radiotherapy treatment planning |
title_sort |
Learning target-based preferences through additive models: An application in radiotherapy treatment planning |
author |
Dias, Luis C. |
author_facet |
Dias, Luis C. Dias, Luis C. Dias, Joana Ventura, Tiago Rocha, Humberto Ferreira, Brígida da Costa Khouri, Leila Lopes, Maria do Carmo Carrilho Calado Antunes Dias, Joana Ventura, Tiago Rocha, Humberto Ferreira, Brígida da Costa Khouri, Leila Lopes, Maria do Carmo Carrilho Calado Antunes |
author_role |
author |
author2 |
Dias, Joana Ventura, Tiago Rocha, Humberto Ferreira, Brígida da Costa Khouri, Leila Lopes, Maria do Carmo Carrilho Calado Antunes |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Dias, Luis C. Dias, Joana Ventura, Tiago Rocha, Humberto Ferreira, Brígida da Costa Khouri, Leila Lopes, Maria do Carmo Carrilho Calado Antunes |
dc.subject.por.fl_str_mv |
Multiple criteria analysis OR in Health Preference disaggregation Radiotherapy |
topic |
Multiple criteria analysis OR in Health Preference disaggregation Radiotherapy |
description |
This article presents a new Multi-Criteria Decision Aiding preference disaggregation method based on an asymmetric target-based model. The decision maker’s preferences are elicited considering the choices made given a set of comparisons among pairs of solutions (the stimuli). It is assumed that the decision maker has a reference value (target) for the stimulus. Solutions that do not comply with this reference value for some of the criteria dimensions considered will be penalized, and an inferred weight is as- sociated with each dimension to calculate a penalty score for each solution. One of the differentiating features of the proposed model when compared with other existing models is the fact that only solu- tions that do not meet the target are penalized. The target is not interpreted as an ideal solution, but as a set of threshold values that should be taken into account when choosing a solution. The proposed ap- proach was applied to the problem of choosing radiotherapy treatment plans, using a set of retrospective cancer cases treated at the Portuguese Oncology Institute of Coimbra. Using paired comparison choices made by one radiation oncologist, the preference model was built and was tested with in-sample and out-of-sample data. It is possible to conclude that the preference model is capable of representing the radiation oncologist’s preferences, presenting small mean errors and leading, most of the time, to the same treatment plan chosen by the radiation oncologist. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/97198 http://hdl.handle.net/10316/97198 https://doi.org/10.1016/j.ejor.2021.12.011 |
url |
http://hdl.handle.net/10316/97198 https://doi.org/10.1016/j.ejor.2021.12.011 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
03772217 https://www.sciencedirect.com/science/article/pii/S0377221721010183 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1822183459227959296 |
dc.identifier.doi.none.fl_str_mv |
10.1016/j.ejor.2021.12.011 |