ArticleNature reviews. Nephrology2019
Artificial intelligence to predict AKI: is it a breakthrough?
Article in Nature reviews. Nephrology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis 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
19 citing papers in PubMed, 1 synthesis or guideline pooled 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
- A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology.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
- An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.BMC medical informatics and decision making · 2025Article
- Review
- Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.Kidney medicine · 2025Review
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Early Diagnosis and Treatment of Kidney Injury: A Focus on Urine Protein.International journal of molecular sciences · 2024Review
- Transforming Hospital Quality Improvement Through Harnessing the Power of Artificial Intelligence.Global journal on quality and safety in healthcare · 2024Article
- Assessment of Risk Factors for Acute Kidney Injury with Machine Learning Tools in Children Undergoing Hematopoietic Stem Cell Transplantation.Journal of clinical medicine · 2024Article
- KIM-1, IL-18, and NGAL, in the Machine Learning Prediction of Kidney Injury among Children Undergoing Hematopoietic Stem Cell Transplantation-A Pilot Study.International journal of molecular sciences · 2023Article
- Predicting in-hospital outcomes of patients with acute kidney injury.Nature communications · 2023Article
- Integration of artificial intelligence and multi-omics in kidney diseases.Fundamental research · 2023Review
- A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients.Journal of clinical medicine · 2022Article
- Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction.Kidney international reports · 2021Article
- Modeling multivariate clinical event time-series with recurrent temporal mechanisms.Artificial intelligence in medicine · 2021Article
- The incidence, mortality and renal outcomes of acute kidney injury in patients with suspected infection at the emergency department.PloS one · 2021Article
- Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction.Nature communications · 2020Article
- Reducing hospital mortality: Incremental change informed by structured mortality review is effective.Future healthcare journal · 2020Article
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2 authors.
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Abstract
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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.