Evaluation of the bias in the management of patient’s appointments in a pediatric office

Autores
Vegega, Cinthia; Pytel, Pablo; Pollo Cattaneo, María Florencia
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The application of Machine Learning algorithms must always take into account the objectives set within the project, the characteristics of the domain where the project will be carried out and the data available to use. Given this, it is essential before collecting data considered as representative of the problem to be solved, because otherwise there may be hidden biases in the data and these may solve a different problem from the one intended. In this context, the aim of this work is to apply a process based on the Gridding method that allows the analysis of the features of the data to be used. This process is applied to the historical data of a pediatric medical office where it is sought to implement an intelligent system that allows to predict the number of normal and overshift appointments for a particular date and time, since it is desired to hire, when necessary, another pediatric doctor to assist in the care of patients.
UTN FRBA
Fil: Vegega, Cinthia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Information System Methodologies Research Group; Argentina.
Fil: Pytel, Pablo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Information System Methodologies Research Group; Argentina.
Fil: Pollo Cattaneo, María Florencia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Information System Methodologies Research Group; Argentina.
Peer Reviewed
Fuente
Paradigm Plus Vol. 1 No. (1) pp 1 - 21. (2020)
Materia
intelligent systems
machine learning
training data
repertory grid
bias
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-03-20T22:46:09Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/9994
Acceder
id RIAUTN_e8f69aff57a08780b8d065ae25827044
oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/9994
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Evaluation of the bias in the management of patient’s appointments in a pediatric officeVegega, CinthiaPytel, PabloPollo Cattaneo, María Florenciaintelligent systemsmachine learningtraining datarepertory gridbiasThe application of Machine Learning algorithms must always take into account the objectives set within the project, the characteristics of the domain where the project will be carried out and the data available to use. Given this, it is essential before collecting data considered as representative of the problem to be solved, because otherwise there may be hidden biases in the data and these may solve a different problem from the one intended. In this context, the aim of this work is to apply a process based on the Gridding method that allows the analysis of the features of the data to be used. This process is applied to the historical data of a pediatric medical office where it is sought to implement an intelligent system that allows to predict the number of normal and overshift appointments for a particular date and time, since it is desired to hire, when necessary, another pediatric doctor to assist in the care of patients.UTN FRBAFil: Vegega, Cinthia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Information System Methodologies Research Group; Argentina.Fil: Pytel, Pablo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Information System Methodologies Research Group; Argentina.Fil: Pollo Cattaneo, María Florencia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Information System Methodologies Research Group; Argentina.Peer Reviewed2024-03-20T22:46:09Z2024-03-20T22:46:09Z2020-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfParadigm Plus Vol. 1http://hdl.handle.net/20.500.12272/999410.55969/paradigm plusParadigm Plus Vol. 1 No. (1) pp 1 - 21. (2020)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacionalenginfo:eu-repo/semantics/openAccess2024-03-20T22:46:09Zhttp://creativecommons.org/licenses/by-nc-sa/4.0/Atribución-NoComercial-CompartirIgual 4.0 InternacionalCinthia Vegega, Pablo Pytel, María Florencia Pollo CattaneoLicencia Creative Commons Atribución- No Comercial2026-10-01T11:57:45Zoai:ria.utn.edu.ar:20.500.12272/9994instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-10-01 11:57:46.866Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Evaluation of the bias in the management of patient’s appointments in a pediatric office
title Evaluation of the bias in the management of patient’s appointments in a pediatric office
spellingShingle Evaluation of the bias in the management of patient’s appointments in a pediatric office
Vegega, Cinthia
intelligent systems
machine learning
training data
repertory grid
bias
title_short Evaluation of the bias in the management of patient’s appointments in a pediatric office
title_full Evaluation of the bias in the management of patient’s appointments in a pediatric office
title_fullStr Evaluation of the bias in the management of patient’s appointments in a pediatric office
title_full_unstemmed Evaluation of the bias in the management of patient’s appointments in a pediatric office
title_sort Evaluation of the bias in the management of patient’s appointments in a pediatric office
dc.creator.none.fl_str_mv Vegega, Cinthia
Pytel, Pablo
Pollo Cattaneo, María Florencia
author Vegega, Cinthia
author_facet Vegega, Cinthia
Pytel, Pablo
Pollo Cattaneo, María Florencia
author_role author
author2 Pytel, Pablo
Pollo Cattaneo, María Florencia
author2_role author
author
dc.subject.none.fl_str_mv intelligent systems
machine learning
training data
repertory grid
bias
topic intelligent systems
machine learning
training data
repertory grid
bias
dc.description.none.fl_txt_mv The application of Machine Learning algorithms must always take into account the objectives set within the project, the characteristics of the domain where the project will be carried out and the data available to use. Given this, it is essential before collecting data considered as representative of the problem to be solved, because otherwise there may be hidden biases in the data and these may solve a different problem from the one intended. In this context, the aim of this work is to apply a process based on the Gridding method that allows the analysis of the features of the data to be used. This process is applied to the historical data of a pediatric medical office where it is sought to implement an intelligent system that allows to predict the number of normal and overshift appointments for a particular date and time, since it is desired to hire, when necessary, another pediatric doctor to assist in the care of patients.
UTN FRBA
Fil: Vegega, Cinthia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Information System Methodologies Research Group; Argentina.
Fil: Pytel, Pablo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Information System Methodologies Research Group; Argentina.
Fil: Pollo Cattaneo, María Florencia. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Information System Methodologies Research Group; Argentina.
Peer Reviewed
description The application of Machine Learning algorithms must always take into account the objectives set within the project, the characteristics of the domain where the project will be carried out and the data available to use. Given this, it is essential before collecting data considered as representative of the problem to be solved, because otherwise there may be hidden biases in the data and these may solve a different problem from the one intended. In this context, the aim of this work is to apply a process based on the Gridding method that allows the analysis of the features of the data to be used. This process is applied to the historical data of a pediatric medical office where it is sought to implement an intelligent system that allows to predict the number of normal and overshift appointments for a particular date and time, since it is desired to hire, when necessary, another pediatric doctor to assist in the care of patients.
publishDate 2020
dc.date.none.fl_str_mv 2020-04-01
2024-03-20T22:46:09Z
2024-03-20T22:46:09Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv Paradigm Plus Vol. 1
http://hdl.handle.net/20.500.12272/9994
10.55969/paradigm plus
identifier_str_mv Paradigm Plus Vol. 1
10.55969/paradigm plus
url http://hdl.handle.net/20.500.12272/9994
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-03-20T22:46:09Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Cinthia Vegega, Pablo Pytel, María Florencia Pollo Cattaneo
Licencia Creative Commons Atribución- No Comercial
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-03-20T22:46:09Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Cinthia Vegega, Pablo Pytel, María Florencia Pollo Cattaneo
Licencia Creative Commons Atribución- No Comercial
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv Paradigm Plus Vol. 1 No. (1) pp 1 - 21. (2020)
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
_version_ 1877862373053693952
score 13.365483

Publicaciones similares