Maria D’costa*
Kidney failure is a critical medical condition affecting millions worldwide. Understanding the longitudinal trajectories of kidney disease progression is essential for effective diagnosis, treatment, and patient management. Traditional data analysis techniques may not fully capture the complex patterns within longitudinal data. However, recent advancements in computational methods have led to the development of innovative web applications that employ trajectory clustering algorithms. This review article explores the significance of such a web application for kidney failure analysis, its potential benefits, challenges, and future prospects within the medical domain.
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