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
- Institución
- Universidad Nacional del Comahue
- OAI Identificador
- oai:rdi.uncoma.edu.ar:uncomaid/18633
Ver los metadatos del registro completo
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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 Comahue2025-09-29T14:28:45Zoai: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:71082025-09-29 14:28:45.922Repositorio 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 |
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score |
12.559606 |