Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Modelling and Simulation 2024 - 38th Annual European Simulation and Modelling Conference 2024, ESM 2024 |
| Editors | Jose David Nunez-Gonzalez, Manuel Grana Romay, Philippe Geril |
| Pages | 110-113 |
| Number of pages | 4 |
| ISBN (Electronic) | 9789492859334 |
| Publication status | Published - 25 Oct 2024 |
Publication series
| Name | Modelling and Simulation 2024 - 38th Annual European Simulation and Modelling Conference 2024, ESM 2024 |
|---|
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- chronic kidney disease
- Data-driven model
- simulation
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