SHAP-Based Explainable Clustering for Medical Records Insights

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, leveragi...

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Autores principales:	Lusso, Adriano Mauricio, Torres, Antonella, Braun, Germán, Gimenez, Christian Nelson
Formato:	documento de conferencia conferenceObject acceptedVersion
Lenguaje:	Inglés
Publicado:	Universidad Nacional del Comahue. Facultad de Informática 2025
Materias:	AI in healthcare Clustering SHAP Póster Ciencias de la Computación e Información
Acceso en línea:	https://rdi.uncoma.edu.ar/handle/uncomaid/18633
Aporte de:	Repositorio Institucional UNCo de Universidad Nacional del Comahue

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spelling	I22-R178-uncomaid-186332025-04-22T12:32:04Z SHAP-Based Explainable Clustering for Medical Records Insights Lusso, Adriano Mauricio Torres, Antonella Braun, Germán Gimenez, Christian Nelson AI in healthcare Clustering SHAP Póster Ciencias de la Computación e Información 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. 2025-03-10 2025-04-11T15:31:44Z 2025-04-11T15:31:44Z documento de conferencia conferenceObject acceptedVersion https://rdi.uncoma.edu.ar/handle/uncomaid/18633 eng https://khipu.ai/khipu2025/poster-sessions-2025/#PosterSession1 Atribución-NoComercial-CompartirIgual 4.0 https://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf application/pdf Universidad Nacional del Comahue. Facultad de Informática Latin American AI Institute
institution	Universidad Nacional del Comahue
institution_str	I-22
repository_str	R-178
collection	Repositorio Institucional UNCo
language	Inglés
topic	AI in healthcare Clustering SHAP Póster Ciencias de la Computación e Información
spellingShingle	AI in healthcare Clustering SHAP Póster Ciencias de la Computación e Información Lusso, Adriano Mauricio Torres, Antonella Braun, Germán Gimenez, Christian Nelson SHAP-Based Explainable Clustering for Medical Records Insights
topic_facet	AI in healthcare Clustering SHAP Póster Ciencias de la Computación e Información
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.
format	documento de conferencia conferenceObject acceptedVersion
author	Lusso, Adriano Mauricio Torres, Antonella Braun, Germán Gimenez, Christian Nelson
author_facet	Lusso, Adriano Mauricio Torres, Antonella Braun, Germán Gimenez, Christian Nelson
author_sort	Lusso, Adriano Mauricio
title	SHAP-Based Explainable Clustering for Medical Records Insights
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
publisher	Universidad Nacional del Comahue. Facultad de Informática
publishDate	2025
url	https://rdi.uncoma.edu.ar/handle/uncomaid/18633
work_keys_str_mv	AT lussoadrianomauricio shapbasedexplainableclusteringformedicalrecordsinsights AT torresantonella shapbasedexplainableclusteringformedicalrecordsinsights AT braungerman shapbasedexplainableclusteringformedicalrecordsinsights AT gimenezchristiannelson shapbasedexplainableclusteringformedicalrecordsinsights
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SHAP-Based Explainable Clustering for Medical Records Insights

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