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Updated: May 7, 2025

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
Machine learning applications in healthcare clinical practice and research
Nikolaos-Achilleas Arkoudis1,2, Stavros P Papadakos3
1Research Unit of Radiology and Medical Imaging, School of Medicine, National and Kapodistrian University of Athens, Athens 11528, Greece.
Machine learning (ML) models identified key factors influencing estimated glomerular filtration rate in women. Age was the primary determinant, with other physiological markers also showing significance, demonstrating ML
Area of Science:
- Artificial Intelligence in Medicine
- Biostatistics
- Nephrology
Background:
- Machine learning (ML) offers advanced data analysis capabilities for healthcare research.
- Non-alcoholic fatty liver disease (NAFLD) may impact kidney function.
- Understanding factors affecting estimated glomerular filtration rate (eGFR) is crucial for clinical practice.
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