ArticleScientific reports2023
Prediction of chronic kidney disease progression using recurrent neural network and electronic health records.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- GERBEHRT: a BERT-based model tailored for German electronic health records - potential in chronic kidney disease prediction.BMC medical informatics and decision making · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Clinical Artificial Intelligence Agents in Nephrology: From Prediction to Action Through Workflow-Native Intelligence-A Roadmap for Workflow-Integrated Care.Journal of clinical medicine · 2026Review
- Risk Prediction of Chronic Kidney Disease Progression in Type 2 Diabetes Mellitus Across Diverse Populations.NPJ digital medicine · 2026Article
- Article
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
- Chronic renal disease detection developments with future potential to improve veterinary clinical practice.Frontiers in veterinary science · 2026Review
- The Mechanism and Application of Traditional Chinese Medicine Nano-Formulations in the Treatment of Renal Fibrosis.International journal of nanomedicine · 2026Review
- Development of an explainable machine learning model for predicting the occurrence of advanced diabetic kidney disease.Frontiers in endocrinology · 2026Article
- Decoding Natural Language Processing and Large Language Models in Kidney Health.Clinical journal of the American Society of Nephrology : CJASN · 2026Article
- Eigen-guided transformer: A data-driven approach for chronic kidney disease forecasting.PloS one · 2026Article
- Comparative analysis of kidney function prediction: traditional statistical methods vs. deep learning techniques.Clinical and experimental nephrology · 2025Article
- The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.Proteomics · 2025Review
- Advances and challenges in kidney fibrosis therapeutics.Nature reviews. Nephrology · 2025Review
- Review
- Early prediction of CKD from time series data using adaptive PSO optimized echo state networks.Scientific reports · 2025Article
- A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.Scientific reports · 2025Article
- Early detection of chronic kidney disease using deep learning: a Mini review.Frontiers in digital health · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.
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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.