ArticleNature communications2020
Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
65 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.BMC medical informatics and decision making · 2025Pooled it
- External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- 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
- Systematic review of externally validated machine learning models for predicting acute kidney injury in general hospital patients.Frontiers in nephrology · 2023Pooled it
- From black box to glass box: explainable artificial intelligence for acute kidney injury prediction-a scoping review and the GLASS-AKI translational framework proposal.International urology and nephrology · 2026Review
- External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data.Critical care explorations · 2026Article
- Development and Validation of Machine Learning Models to Identify Emergency Department Patients at Increased Risk of New or Progressive Acute Kidney Injury.Journal of the American College of Emergency Physicians open · 2026Article
- Exploring the limits of localization: federated model stacking improves hospital-level prediction in a national research network.NPJ digital medicine · 2026Article
- Emerging Urinary Biomarkers and Innovative Technologies for the Early Detection and Personalized Management of Chronic Kidney Disease.International journal of molecular sciences · 2026Review
- Predicting Acute Kidney Injury with Nephrotoxic Burden in Noncritical Patients: An Internal and External Validation Study.Kidney360 · 2026Article
- Predicting and Modeling Recovery Dynamics Post-Acute Kidney Injury: A Multicenter Study.Kidney360 · 2026Article
- 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
- Early risk stratification for carbapenem resistance amongFrontiers in cellular and infection microbiology · 2026Article
- SMART: a new patient similarity estimation framework for enhanced predictive modeling in acute kidney injury.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Ngf/TrKA signal is associated with macrophage activation of acute kidney injury in mice.Renal failure · 2025Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study.Journal of medical Internet research · 2025Article
- Multicenter validation of a scalable, interpretable, multitask prediction model for multiple clinical outcomes.NPJ digital medicine · 2025Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Artificial intelligence (AI) has demonstrated promise in predicting acute kidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability. Non-interoperable data across hospitals is a major barrier to model transportability. Here, we leverage the US PCORnet platform to develop an AKI prediction model and assess its transportability across six independent health systems. Our work demonstrates that cross-site performance deterioration is likely and reveals heterogeneity of risk factors across populations to be the cause. Therefore, no matter how accurate an AI model is trained at the source hospital, whether it can be adopted at target hospitals is an unanswered question. To fill the research gap, we derive a method to predict the transportability of AI models which can accelerate the adaptation process of external AI models in hospitals.
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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.