Machine learning in nephrology: scratching the surface

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.

Current status of China's critical care medicine big data platform and future prospects

“…So far, it has profound applications in the various specialties of medicine. [2][3][4] However, intensive care medicine (ICU) is different from other medical fields. In comparison with clinical practice data, medical data in ICU have the following characteristics: large scale, rapid production, diverse dimensions, inaccuracies, heterogeneity, incompleteness, complexity, and privacy concerns.…”

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

“…The model established by machine learning approaches can effectuate early dynamic monitoring based on the actual objective data of all patients and conserve the time of clinicians. [61][62][63][64]. The rise of machine learning is driven by the ability to process "big data" and the need to deliver the best possible value-and evidence-based care.…”

“…The rise of machine learning is driven by the ability to process “big data” and the need to deliver the best possible value- and evidence-based care. The utility of artificial intelligence (AI) coupled with machine learning, has generated much interest and many studies in clinical medicine [ 61 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. The machine learning approach has been developed recently for advantages in performance and extensibility and has become indispensable for solving complex problems in most sciences [ 80 , 81 , 82 ].…”

Machine-Learning Model to Predict the Intradialytic Hypotension Based on Clinical-Analytical Data

“…Although the application of ML models is relatively mature in other specialties, this has not been the case in the field of nephrology, as the lack of evidence and the limited scope of research in kidney disease have not allowed this specialty to benefit from these technologies [15], [16]. Therefore, the development of intelligent ML-based models may support nephrology medical staff in the context of identifying the occurrence of IDH in patients receiving HD.…”