ArticleJournal of personalized medicine2022
Artificial Intelligence for Risk Prediction of Rehospitalization with Acute Kidney Injury in Sepsis Survivors.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 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
- Acute kidney injury prediction and prognostication using machine learning.International urology and nephrology · 2026Review
- Machine learning-based prediction of a high-risk kidney function trajectory class after acute kidney injury.BMJ health & care informatics · 2026Article
- Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods.Scientific reports · 2025Article
- Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.Renal failure · 2024Article
- Review
- Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities.International journal of environmental research and public health · 2024Review
- Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults.Scientific reports · 2023Article
- Personalized Prediction of Patient Radiation Exposure for Therapy of Urolithiasis: An Application and Comparison of Six Machine Learning Algorithms.Journal of personalized medicine · 2023Article
- Role of C-Reactive Protein in Kidney Diseases.Kidney diseases (Basel, Switzerland) · 2023Review
- Liver Injury in Favipiravir-Treated COVID-19 Patients: Retrospective Single-Center Cohort Study.Tropical medicine and infectious disease · 2023Article
- Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects.Kidney diseases (Basel, Switzerland)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Sepsis survivors have a higher risk of long-term complications. Acute kidney injury (AKI) may still be common among sepsis survivors after discharge from sepsis. Therefore, our study utilized an artificial-intelligence-based machine learning approach to predict future risks of rehospitalization with AKI between 1 January 2008 and 31 December 2018. We included a total of 23,761 patients aged ≥ 20 years who were admitted due to sepsis and survived to discharge. We adopted a machine learning method by using models based on logistic regression, random forest, extra tree classifier, gradient boosting decision tree (GBDT), extreme gradient boosting, and light gradient boosting machine (LGBM). The LGBM model exhibited the highest area under the receiver operating characteristic curves (AUCs) of 0.816 to predict rehospitalization with AKI in sepsis survivors and followed by the GBDT model with AUCs of 0.813. The top five most important features in the LGBM model were C-reactive protein, white blood cell counts, use of inotropes, blood urea nitrogen and use of diuretics. We established machine learning models for the prediction of the risk of rehospitalization with AKI in sepsis survivors, and the machine learning model may set the stage for the broader use of clinical features in healthcare.
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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.