ArticleClinical kidney journal2021
Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.
Article in Clinical kidney journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.Critical care (London, England) · 2025Article
- Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study.Journal of medical Internet research · 2025Article
- Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Artificial intelligence in nephrology education: a multicenter survey of fellowship trainees at Mayo Clinic.Frontiers in nephrology · 2025Article
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Role of artificial intelligence in critical care nutrition support and research.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2024Review
- Anesthetic Management Recommendations Using a Machine Learning Algorithm to Reduce the Risk of Acute Kidney Injury After Cardiac Surgeries.Anesthesiology and pain medicine · 2024Article
- Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study.EClinicalMedicine · 2024Article
- Machine-learning model for predicting oliguria in critically ill patients.Scientific reports · 2024Article
- Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis.Journal of multidisciplinary healthcare · 2024Article
- Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy.BMC medical informatics and decision making · 2023Article
- Closed-Loop Medication Management with an Electronic Health Record System in U.S. and Finnish Hospitals.International journal of environmental research and public health · 2023Review
- Accurate and interpretable prediction of ICU-acquired AKI.Journal of critical care · 2023Article
- Early prediction of acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit based on extreme gradient boosting.Frontiers in medicine · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.Advances in chronic kidney disease · 2022Review
- Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning.Frontiers in surgery · 2022Article
- KGDAL: Knowledge Graph Guided Double Attention LSTM for Rolling Mortality Prediction for AKI-D Patients.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the intensive care unit (ICU). Our purpose in this study is to develop and externally validate a model for predicting AKI in the ICU using patient data present prior to ICU admission.
methodsWe used data of 98 472 adult ICU admissions at Mayo Clinic between 1 January 2005 and 31 December 2017 and 51 801 encounters from Medical Information Mart for Intensive Care III (MIMIC-III) cohort. A gradient-boosting model was trained on 80% of the Mayo Clinic cohort using a set of features to predict AKI acquired in the ICU.
resultsAKI was identified in 39 307 (39.9%) encounters in the Mayo Clinic cohort. Patients who developed AKI in the ICU were older and had higher ICU and in-hospital mortality compared to patients without AKI. A 30-feature model yielded an area under the receiver operating curve of 0.690 [95% confidence interval (CI) 0.682-0.697] in the Mayo Clinic cohort set and 0.656 (95% CI 0.648-0.664) in the MIMIC-III cohort.
conclusionsUsing machine learning, AKI among ICU patients can be predicted using information available prior to admission. This model is independent of ICU information, making it valuable for stratifying patients at admission.
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