**About the Editors**

**Wisit Cheungpasitporn** is American board certified in Nephrology and Internal Medicine. He completed his nephrology fellowship training at Mayo Clinic, Rochester, Minnesota. Here, Dr. Cheungpasitporn also completed additional training and has become an expert on kidney transplantation. He also completed a postdoctoral appointment as part of the clinical and translational science (CCaTS) program in 2015. Dr. Cheungpasitporn received the 2016 Donald C. Balfour Research Award, given in recognition of outstanding research as a junior scientist whose primary training is in a clinical field at Mayo Clinic, Rochester, Minnesota, as well as the 2016 William H.J. Summerskill Award, given in recognition of outstanding achievement in research for a clinical fellow at Mayo Clinic, Rochester, Minnesota. Dr. Cheungpasitporn joined the Division of Nephrology and Hypertension at Mayo Clinic, where he has served since August 2020.

**Charat Thongprayoon** MD; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA. Email: charat.thongprayoon@gmail.com. Dr. Charat Thongprayoon, MD is affiliated with Mayo Clinic Hospital, Rochester. Interests: nephrology; electrolytes; acute kidney injury; renal replacement therapy; epidemiology; outcome study.

**Wisit Kaewput** MD; Department of Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand. Email: wisitnephro@gmail.com. Dr. Wisit Kaewput is affiliated with Phramongkutklao College of Medicine, Bangkok, Thailand. Interests: acute kidney injury; observational studies; statistical analysis; epidemiology.

## *Editorial* **Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation**

**Charat Thongprayoon 1, Wisit Kaewput 2, Karthik Kovvuru 3, Panupong Hansrivijit 4, Swetha R. Kanduri 3, Tarun Bathini 5, Api Chewcharat 1, Napat Leeaphorn 6, Maria L. Gonzalez-Suarez 3 and Wisit Cheungpasitporn 3,\***


Received: 1 April 2020; Accepted: 9 April 2020; Published: 13 April 2020

**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 grea<sup>t</sup> 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 grea<sup>t</sup> 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 grea<sup>t</sup> emphasis on the usage of nephrology and transplantation.

**Keywords:** artificial intelligence; machine learning; big data; nephrology; transplantation; kidney transplantation; acute kidney injury; chronic kidney disease
