Machine learning in nephrology: scratching the surface
| Autor: | Qi Li, Qiu-Ling Fan, Qiu-Xia Han, Wen-Jia Geng, Huan-Huan Zhao, Xiao-Nan Ding, Jing-Yao Yan, Han-Yu Zhu, Yuan-Yuan Ji |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Chinese Medical Journal, Iss 6, Pp 687-698 (2020) |
| Druh dokumentu: | article |
| ISSN: | 0366-6999 2542-5641 00000000 |
| DOI: | 10.1097/CM9.0000000000000694 |
| Popis: | Abstract. 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. |
| Databáze: | Directory of Open Access Journals |
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