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
.jpg)
- 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 Comahue2025-10-23T11:16:48Zoai: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 |
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2025-03-10 |
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info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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conferenceObject |
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https://rdi.uncoma.edu.ar/handle/uncomaid/18633 |
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eng |
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eng |
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Universidad Nacional del Comahue. Facultad de Informática Latin American AI Institute |
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Universidad Nacional del Comahue. Facultad de Informática Latin American AI Institute |
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