ArticleJournal of translational medicine2017
Improving precision of glomerular filtration rate estimating model by ensemble learning.
Article in Journal of translational medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 35 citations in OpenAlex.
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
- A fused weighted federated learning-based adaptive approach for early-stage drug prediction.Scientific reports · 2025Article
- Comparing methods for glomerular filtration rate estimation.Journal of clinical and translational science · 2025Article
- Height prediction of individuals with osteogenesis imperfecta by machine learning.Orphanet journal of rare diseases · 2024Article
- Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation.medRxiv : the preprint server for health sciences · 2024Article
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- Integration of artificial intelligence and multi-omics in kidney diseases.Fundamental research · 2023Review
- Article
- Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera.Annals of nuclear medicine · 2021Article
- The ensemble learning model is not better than the Asian modified CKD-EPI equation for glomerular filtration rate estimation in Chinese CKD patients in the external validation study.BMC nephrology · 2021Article
- Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study.International urology and nephrology · 2021Article
- Artificial intelligence and machine learning in nephropathology.Kidney international · 2020Review
- Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis.Journal of personalized medicine · 2020Review
- Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation.Journal of translational medicine · 2020Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAccurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise.
methodsWe hypothesized that ensemble learning could improve precision. A total of 1419 participants were enrolled, with 1002 in the development dataset and 417 in the external validation dataset. GFR was independently estimated from age, sex and serum creatinine using an artificial neural network (ANN), support vector machine (SVM), regression, and ensemble learning. GFR was measured by 99mTc-DTPA renal dynamic imaging calibrated with dual plasma sample 99mTc-DTPA GFR.
resultsMean measured GFRs were 70.0 ml/min/1.73 m
conclusionsAn ensemble learning model including three variables, the average ANN, SVM, and regression equation values, was more precise than the new regression model. A more complex ensemble learning strategy may further improve GFR estimates.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.