ArticleNPJ digital medicine2020
Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 6 of them syntheses that pooled it.
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Who cites it
65 citing papers in PubMed, 6 syntheses or guidelines pooled it, 159 citations in OpenAlex.
- Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.BMC medical informatics and decision making · 2024Pooled it
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.BMC nephrology · 2022Pooled it
- Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis.European radiology · 2022Pooled it
- Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis.Frontiers in cardiovascular medicine · 2022Pooled it
- Strategies to Prevent Acute Kidney Injury after Pediatric Cardiac Surgery: A Network Meta-Analysis.Clinical journal of the American Society of Nephrology : CJASN · 2021Pooled it
- Development and validation of the creatinine clearance predictor machine learning models in critically ill adults.Critical care (London, England) · 2023Trial
- Discovery of postoperative clinical states linked to cardiac surgery-associated acute kidney injury.BioData mining · 2026Article
- EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study.Alzheimer's research & therapy · 2026Article
- Deep learning models for acute kidney injury prediction: multi-center external validation and evaluation under simulated continuous monitoring conditions.NPJ digital medicine · 2026Article
- Study protocol for a consortium linking health medical records, biospecimens, and biosignals in Korean patients with acute kidney injury (LINKA cohort).Kidney research and clinical practice · 2026Article
- Augmenting Electronic Health Records for Adverse Event Detection.medRxiv : the preprint server for health sciences · 2026Article
- Development and Validation of a Machine Learning Model for Predicting Serum Creatinine-Defined Acute Kidney Injury in Older Adults After Cardiac Surgery.Clinical interventions in aging · 2026Article
- Temporal Recurrent Neural Networks for Predicting Acute Kidney Injury Recovery by Time of Discharge.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- Leveraging large language models for preoperative prevention of cardiopulmonary bypass-associated acute kidney injury.Renal failure · 2025Article
- Multicenter validation of a scalable, interpretable, multitask prediction model for multiple clinical outcomes.NPJ digital medicine · 2025Article
- Conventional machine learning-based prediction models did not outperform the International IgA Nephropathy Prediction Tool.Kidney research and clinical practice · 2025Article
- Review
- Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early prediction of AKI could prompt preventive measures, but is challenging in the clinical routine. One important reason is that the amount of postoperative data is too massive and too high-dimensional to be effectively processed by the human operator. We therefore sought to develop a deep-learning-based algorithm that is able to predict postoperative AKI prior to the onset of symptoms and complications. Based on 96 routinely collected parameters we built a recurrent neural network (RNN) for real-time prediction of AKI after cardiothoracic surgery. From the data of 15,564 admissions we constructed a balanced training set (2224 admissions) for the development of the RNN. The model was then evaluated on an independent test set (350 admissions) and yielded an area under curve (AUC) (95% confidence interval) of 0.893 (0.862-0.924). We compared the performance of our model against that of experienced clinicians. The RNN significantly outperformed clinicians (AUC = 0.901 vs. 0.745,
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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.