Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions

Gogoi, Prokash; Valan, J. Arul

doi:10.1007/s11255-024-04281-5

Int Urol Nephrol

2024

DOI: 10.1007/s11255-024-04281-5

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Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions

Prokash Gogoi,

J. Arul Valan

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Decoding Kidney Pathophysiology: Omics-Driven Approaches in Precision Medicine

Delrue,

Speeckaert

2024

JPM

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Chronic kidney disease (CKD) is a major worldwide health concern because of its progressive nature and complex biology. Traditional diagnostic and therapeutic approaches usually fail to account for disease heterogeneity, resulting in low efficacy. Precision medicine offers a novel approach to studying kidney disease by combining omics technologies such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics. By identifying discrete disease subtypes, molecular biomarkers, and therapeutic targets, these technologies pave the way for personalized treatment approaches. Multi-omics integration has enhanced our understanding of CKD by revealing intricate molecular linkages and pathways that contribute to treatment resistance and disease progression. While pharmacogenomics offers insights into expected responses to personalized treatments, single-cell and spatial transcriptomics can be utilized to investigate biological heterogeneity. Despite significant development, challenges persist, including data integration concerns, high costs, and ethical quandaries. Standardized data protocols, collaborative data-sharing frameworks, and advanced computational tools such as machine learning and causal inference models are required to address these challenges. With the advancement of omics technology, nephrology may benefit from improved diagnostic accuracy, risk assessment, and personalized care. By overcoming these barriers, precision medicine has the potential to develop novel techniques for improving patient outcomes in kidney disease treatment.

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Decoding Kidney Pathophysiology: Omics-Driven Approaches in Precision Medicine

Delrue,

Speeckaert

2024

JPM

View full text Add to dashboard Cite

show abstract

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Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions

Cited by 1 publication

References 71 publications

Decoding Kidney Pathophysiology: Omics-Driven Approaches in Precision Medicine

Decoding Kidney Pathophysiology: Omics-Driven Approaches in Precision Medicine

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