Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation
Open Access
- 12 April 2020
- journal article
- editorial
- Published by MDPI AG in Journal of Clinical Medicine
- Vol. 9 (4), 1107
- https://doi.org/10.3390/jcm9041107
Abstract
Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.This publication has 122 references indexed in Scilit:
- Associations of sugar‐sweetened and artificially sweetened soda with chronic kidney disease: A systematic review and meta‐analysisNephrology, 2014
- Optimization of anemia treatment in hemodialysis patients via reinforcement learningArtificial Intelligence in Medicine, 2014
- Big Data in Organ Transplantation: Registries and Administrative ClaimsAmerican Journal of Transplantation, 2014
- The Landscape of Clinical Trials in Nephrology: A Systematic Review of ClinicalTrials.govAmerican Journal of Kidney Diseases, 2014
- Chronic kidney disease: global dimension and perspectivesThe Lancet, 2013
- Cohort Profile: The Chronic Kidney Disease Prognosis ConsortiumInternational Journal of Epidemiology, 2012
- Genome-wide association studies of chronic kidney disease: what have we learned?Nature Reviews Nephrology, 2011
- A Predictive Model for Progression of Chronic Kidney Disease to Kidney FailureJAMA, 2011
- PKDB: Polycystic Kidney Disease Mutation Database-a gene variant database for autosomal dominant polycystic kidney diseaseHuman Mutation, 2007
- Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learningNature Medicine, 2002