Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

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.

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

Figure 1

(a) Relationships between artificial intelligence, machine learning, and deep learning. (b) Types of machine learning. CNN, convolutional neural network; RNN, recurrent neural network.