Data-Driven Model for Chronic Kidney Disease Progression: A Work in Progress

Candelaria Alvarez, Remo Suppi, Jose Ibeas, Javier Balladini

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Resumen

In medicine, data-driven models can be used to simulate disease progression and generate clinical decision support systems (CDSS). While artificial intelligence (AI) and machine learning (ML) models are common, their lack of traceability poses challenges in medical contexts, where transparency is crucial. This study aims to create a traceable data-driven model for Chronic Kidney Disease (CKD) progression without using AI or ML. The study will develop and validate a simulator based on this model with real-world data. This paper describes the development of the model, the processing of CKD patient data, the implementation of the simulator, and the validation of results.

Idioma originalInglés
Título de la publicación alojadaModelling and Simulation 2024 - 38th Annual European Simulation and Modelling Conference 2024, ESM 2024
EditoresJose David Nunez-Gonzalez, Manuel Grana Romay, Philippe Geril
Páginas110-113
Número de páginas4
ISBN (versión digital)9789492859334
EstadoPublicada - 25 oct 2024

Serie de la publicación

NombreModelling and Simulation 2024 - 38th Annual European Simulation and Modelling Conference 2024, ESM 2024

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