Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects

Abstract

The emergence of artificial intelligence and machine learning (ML) has revolutionized the landscape of clinical medicine, offering opportunities to improve medical practice and research. This narrative review explores the current status and prospects of applying ML to chronic kidney disease (CKD). ML, at the intersection of statistics and computer science, enables computers to derive insights from extensive datasets, thereby presenting an interesting landscape for constructing statistical models and improving data interpretation. The integration of ML into clinical algorithms aims to increase efficiency and promote its adoption as a standard approach to data interpretation in nephrology. As the field of ML continues to evolve, collaboration between clinicians and data scientists is essential for defining data-sharing and usage policies, ultimately contributing to the advancement of precision diagnostics and personalized medicine in the context of CKD.

Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches

“…AI and ML techniques have emerged as powerful tools in the field of healthcare, offering new avenues for early disease detection, risk prediction, and personalized treatment strategies. In the context of CKD, these advanced analytical approaches hold significant promise in predicting disease progression and identifying high-risk individuals, ultimately facilitating timely interventions and improved patient outcomes [12,24,25].…”

“…Firstly, accurate prediction models can assist healthcare providers in identifying individuals at high-risk of CKD progression, enabling early interventions and personalized treatment strategies. This proactive approach can potentially slow disease progression and delay the need for RRT, improving patient outcomes and quality of life [12,13].…”