Big Data in Nephrology
Author:
Publisher
Springer Science and Business Media LLC
Subject
Nephrology
Link
https://www.nature.com/articles/s41581-021-00439-x.pdf
Reference148 articles.
1. Erickson, K. F., Qureshi, S. & Winkelmayer, W. C. The role of big data in the development and evaluation of US dialysis care. Am. J. Kidney Dis. 72, 560–568 (2018).
2. Adimadhyam, S. et al. Leveraging the capabilities of the FDA’s sentinel system to improve kidney care. J. Am. Soc. Nephrol. 31, 2506–2516 (2020).
3. Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).
4. Escobar, G. J. et al. Automated identification of adults at risk for in-hospital clinical deterioration. N. Engl. J. Med. 383, 1951–1960 (2020).
5. Hulsen, T. et al. From big data to precision medicine. Front. Med. 6, 34 (2019).
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1. Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics;Frontiers in Nephrology;2023-10-26
2. Predicting Renal Toxicity of Compounds with Deep Learning and Machine Learning Methods;SN Computer Science;2023-10-21
3. Development, External Validation, and Visualization of Machine Learning Models for Predicting Occurrence of Acute Kidney Injury after Cardiac Surgery;Reviews in Cardiovascular Medicine;2023-08-09
4. Blockchain in nephrology;Nature Reviews Nephrology;2023-03-24
5. Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance;PLOS ONE;2023-02-23
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