Open AccessMachine learning in nephrology: scratching the surface
Author(s) -
Qi Li,
Qiuling Fan,
Qiuxia Han,
Geng Wang,
Huanhuan Zhao,
Xiaonan Ding,
Jing Yan,
Hanyu Zhu
Publication year - 2020
Publication title -
chinese medical journal/chinese medical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 63
eISSN - 2542-5641
pISSN - 0366-6999
DOI - 10.1097/cm9.0000000000000694
Subject(s) - nephrology , medicine , machine learning , artificial intelligence , intensive care medicine , dialysis , acute kidney injury , computer science
Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.