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Leveraging Multimodal Deep Learning and Explainable Artificial Intelligence for Early Detection of Kidney Failure in Ghana

Domaine:

healthcare

Type de record:

model
Créateur:
Sab
Éditeur:
Cen
Éditeur:
OSF
Hôte:avatar
This study develops and validates a deep learning model for the early detection and prediction of kidney failure progression among Ghanaian patients. Using multimodal clinical data from healthcare facilities in Ghana, the model will be trained on biochemical, demographic, and imaging variables and evaluated against traditional machine learning approaches. Explainable Artificial Intelligence (XAI) techniques will be applied to ensure model predictions are transparent and clinically interpretable. The study addresses a critical gap in the literature, as few deep learning framework has been developed and validated specifically on Ghanaian kidney disease data. Expected outcomes include a validated predictive model, identification of key local predictors of kidney failure, and an open-access codebase to support future research across sub-Saharan Africa.

Visit

doi.orgosf.io

Tags

Medicine and Health SciencesKidney Failure Chronic Kidney Disease Deep Learning Explainable Artificial Intelligence SHAP Multimodal Clinical Data Early Detection Ghana Sub-Saharan Africa Nephrology Medical Informatics Precision Medicine eGFR Predictive Modelling Global Health

Licenses

Creative Commons Zero v1.0 Universalhttps://creativecommons.org/publicdomain/zero/1.0/legalcode

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