Role of artificial intelligence in the diagnosis and management of kidney disease: applications to chronic kidney disease and acute kidney injury

肾脏疾病 重症监护医学 危害 急性肾损伤 医学 疾病 梅德林 人工智能 计算机科学 内科学 心理学 政治学 社会心理学 法学
作者
Navdeep Tangri,Thomas Ferguson
出处
期刊:Current Opinion in Nephrology and Hypertension [Ovid Technologies (Wolters Kluwer)]
卷期号:31 (3): 283-287 被引量:6
标识
DOI:10.1097/mnh.0000000000000787
摘要

Chronic kidney disease (CKD) and acute kidney injury (AKI) are global public health problems associated with a significant burden of morbidity, healthcare resource use, and all-cause mortality. This review explores recently published studies that take a machine learning approach to the diagnosis, management, and prognostication in patients with AKI or CKD.The release of novel therapeutics for CKD has highlighted the importance of accurately identifying patients at the highest risk of progression. Many models have been constructed with reasonable predictive accuracy but have not been extensively externally validated and peer reviewed. Similarly, machine learning models have been developed for prediction of AKI and have found sufficient accuracy. There are issues to implementing these models, however, with conflicting results with respect to the relationship between prediction of an AKI outcome and improvements in the occurrence of other adverse events, and in some circumstances potential harm.Artificial intelligence models can help guide management of CKD and AKI, but it is important to ensure that they are broadly applicable and generalizable to various settings and associated with improved clinical decision-making and outcomes.
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