SHAP-Based Explainable Clustering for Medical Records Insights

Autores
Lusso, Adriano Mauricio; Torres, Antonella; Braun, Germán; Gimenez, Christian Nelson
Año de publicación
2025
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión aceptada
Descripción
Machine Learning is a fundamental tool for information analysis. Among its various techniques, clustering stands out as a family of algorithms capable of dividing large datasets into distinct groups based on similarity. In the healthcare domain, state-of-the-art research has been conducted, leveraging the vast availability of patient medical data, which makes clustering a powerful tool for knowledge discovery. However, Machine Learning also presents limitations, such as difficulties in explaining its results and the potential for unethical biases, which pose significant challenges for real-world applications. This study explores opportunities for applying clustering techniques within the Social Security Insurance system of Universidad Nacional del Comahue, a university located in Neuquén, Argentina. Additionally, clustering will be combined with the SHAP framework to enhance the explainability of the obtained results.
Fil: Lusso, Adriano Mauricio. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.
Fil: Torres, Antonella. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.
Fil: Braun, Germán. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.
Fil: Gimenez, Christian Nelson. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.
Materia
AI in healthcare
Clustering
SHAP
Póster
Ciencias de la Computación e Información
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Repositorio Digital Institucional (UNCo)
Institución
Universidad Nacional del Comahue
OAI Identificador
oai:rdi.uncoma.edu.ar:uncomaid/18633
Acceder
id RDIUNCO_9441dfe8db8aa3b71a26af8c1864d8fd
oai_identifier_str oai:rdi.uncoma.edu.ar:uncomaid/18633
network_acronym_str RDIUNCO
repository_id_str 7108
network_name_str Repositorio Digital Institucional (UNCo)
spelling SHAP-Based Explainable Clustering for Medical Records InsightsLusso, Adriano MauricioTorres, AntonellaBraun, GermánGimenez, Christian NelsonAI in healthcareClusteringSHAPPósterCiencias de la Computación e InformaciónMachine Learning is a fundamental tool for information analysis. Among its various techniques, clustering stands out as a family of algorithms capable of dividing large datasets into distinct groups based on similarity. In the healthcare domain, state-of-the-art research has been conducted, leveraging the vast availability of patient medical data, which makes clustering a powerful tool for knowledge discovery. However, Machine Learning also presents limitations, such as difficulties in explaining its results and the potential for unethical biases, which pose significant challenges for real-world applications. This study explores opportunities for applying clustering techniques within the Social Security Insurance system of Universidad Nacional del Comahue, a university located in Neuquén, Argentina. Additionally, clustering will be combined with the SHAP framework to enhance the explainability of the obtained results.Fil: Lusso, Adriano Mauricio. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.Fil: Torres, Antonella. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.Fil: Braun, Germán. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.Fil: Gimenez, Christian Nelson. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.Universidad Nacional del Comahue. Facultad de InformáticaLatin American AI Institute2025-03-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttps://rdi.uncoma.edu.ar/handle/uncomaid/18633enghttps://khipu.ai/khipu2025/poster-sessions-2025/#PosterSession1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/reponame:Repositorio Digital Institucional (UNCo)instname:Universidad Nacional del Comahue2025-10-23T11:16:48Zoai:rdi.uncoma.edu.ar:uncomaid/18633instacron:UNCoInstitucionalhttp://rdi.uncoma.edu.ar/Universidad públicaNo correspondehttp://rdi.uncoma.edu.ar/oaimirtha.mateo@biblioteca.uncoma.edu.ar; adriana.acuna@biblioteca.uncoma.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:71082025-10-23 11:16:48.857Repositorio Digital Institucional (UNCo) - Universidad Nacional del Comahuefalse
dc.title.none.fl_str_mv SHAP-Based Explainable Clustering for Medical Records Insights
title SHAP-Based Explainable Clustering for Medical Records Insights
spellingShingle SHAP-Based Explainable Clustering for Medical Records Insights
Lusso, Adriano Mauricio
AI in healthcare
Clustering
SHAP
Póster
Ciencias de la Computación e Información
title_short SHAP-Based Explainable Clustering for Medical Records Insights
title_full SHAP-Based Explainable Clustering for Medical Records Insights
title_fullStr SHAP-Based Explainable Clustering for Medical Records Insights
title_full_unstemmed SHAP-Based Explainable Clustering for Medical Records Insights
title_sort SHAP-Based Explainable Clustering for Medical Records Insights
dc.creator.none.fl_str_mv Lusso, Adriano Mauricio
Torres, Antonella
Braun, Germán
Gimenez, Christian Nelson
author Lusso, Adriano Mauricio
author_facet Lusso, Adriano Mauricio
Torres, Antonella
Braun, Germán
Gimenez, Christian Nelson
author_role author
author2 Torres, Antonella
Braun, Germán
Gimenez, Christian Nelson
author2_role author
author
author
dc.subject.none.fl_str_mv AI in healthcare
Clustering
SHAP
Póster
Ciencias de la Computación e Información
topic AI in healthcare
Clustering
SHAP
Póster
Ciencias de la Computación e Información
dc.description.none.fl_txt_mv Machine Learning is a fundamental tool for information analysis. Among its various techniques, clustering stands out as a family of algorithms capable of dividing large datasets into distinct groups based on similarity. In the healthcare domain, state-of-the-art research has been conducted, leveraging the vast availability of patient medical data, which makes clustering a powerful tool for knowledge discovery. However, Machine Learning also presents limitations, such as difficulties in explaining its results and the potential for unethical biases, which pose significant challenges for real-world applications. This study explores opportunities for applying clustering techniques within the Social Security Insurance system of Universidad Nacional del Comahue, a university located in Neuquén, Argentina. Additionally, clustering will be combined with the SHAP framework to enhance the explainability of the obtained results.
Fil: Lusso, Adriano Mauricio. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.
Fil: Torres, Antonella. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.
Fil: Braun, Germán. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.
Fil: Gimenez, Christian Nelson. Universidad Nacional del Comahue. Facultad de Informática. Grupo de Investigación en Lenguajes e Inteligencia Artificial; Argentina.
description Machine Learning is a fundamental tool for information analysis. Among its various techniques, clustering stands out as a family of algorithms capable of dividing large datasets into distinct groups based on similarity. In the healthcare domain, state-of-the-art research has been conducted, leveraging the vast availability of patient medical data, which makes clustering a powerful tool for knowledge discovery. However, Machine Learning also presents limitations, such as difficulties in explaining its results and the potential for unethical biases, which pose significant challenges for real-world applications. This study explores opportunities for applying clustering techniques within the Social Security Insurance system of Universidad Nacional del Comahue, a university located in Neuquén, Argentina. Additionally, clustering will be combined with the SHAP framework to enhance the explainability of the obtained results.
publishDate 2025
dc.date.none.fl_str_mv 2025-03-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://rdi.uncoma.edu.ar/handle/uncomaid/18633
url https://rdi.uncoma.edu.ar/handle/uncomaid/18633
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://khipu.ai/khipu2025/poster-sessions-2025/#PosterSession1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional del Comahue. Facultad de Informática
Latin American AI Institute
publisher.none.fl_str_mv Universidad Nacional del Comahue. Facultad de Informática
Latin American AI Institute
dc.source.none.fl_str_mv reponame:Repositorio Digital Institucional (UNCo)
instname:Universidad Nacional del Comahue
reponame_str Repositorio Digital Institucional (UNCo)
collection Repositorio Digital Institucional (UNCo)
instname_str Universidad Nacional del Comahue
repository.name.fl_str_mv Repositorio Digital Institucional (UNCo) - Universidad Nacional del Comahue
repository.mail.fl_str_mv mirtha.mateo@biblioteca.uncoma.edu.ar; adriana.acuna@biblioteca.uncoma.edu.ar
_version_ 1846787656313208832
score 12.982451

Publicaciones similares