2022
DOI: 10.1186/s12911-022-01951-1
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Machine learning algorithms’ accuracy in predicting kidney disease progression: a systematic review and meta-analysis

Abstract: Background Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression. … Show more

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Cited by 16 publications
(11 citation statements)
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“…By harnessing the power of data and technologies—including artificial intelligence and machine learning—value‐based kidney care can directly improve costs and quality of care. Emerging research affirms the role machine learning can play in evaluating CKD prognosis and predicting CKD progression 60–62 . Predictive modeling holds particular promise in: (1) prioritizing patient outreach, (2) anticipating unplanned dialysis starts, and (3) enhancing patient choice at end‐of‐life.…”
Section: Optimizing Value‐based Kidney Carementioning
confidence: 98%
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“…By harnessing the power of data and technologies—including artificial intelligence and machine learning—value‐based kidney care can directly improve costs and quality of care. Emerging research affirms the role machine learning can play in evaluating CKD prognosis and predicting CKD progression 60–62 . Predictive modeling holds particular promise in: (1) prioritizing patient outreach, (2) anticipating unplanned dialysis starts, and (3) enhancing patient choice at end‐of‐life.…”
Section: Optimizing Value‐based Kidney Carementioning
confidence: 98%
“…Emerging research affirms the role machine learning can play in evaluating CKD prognosis and predicting CKD progression. [60][61][62] Predictive modeling holds particular promise in: (1) prioritizing patient outreach, (2) anticipating unplanned dialysis starts, and (3) enhancing patient choice at end-of-life. The impact of these technologies may manifest most clearly among patient populations with high disease burden or polychronic illness.…”
Section: Predictive Modelingmentioning
confidence: 99%
“…In order to promote timely identification of patients at high risk of kidney function deterioration, researchers have developed several disease prediction models. Although these models performed well at internal validation, they present an uncertain generalization capability due in part to the use of non-public datasets, such as medical images or clinical data from EHR [22], which hinders benchmarking of the models [8].…”
Section: Related Workmentioning
confidence: 99%
“…[4]. In addition, the early identification of groups at high risk of CKD has become an important focus in kidney disease management strategies [8].…”
Section: Introductionmentioning
confidence: 99%
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