ArticleScientific reports2018
Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Feature Ranking on Small Samples: A Bayes-Based Approach.Entropy (Basel, Switzerland) · 2025Article
- An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.BMC medical informatics and decision making · 2025Article
- A two-tier feature selection method for predicting mortality risk in ICU patients with acute kidney injury.Scientific reports · 2024Article
- Internal and External Validation of Machine Learning Models for Predicting Acute Kidney Injury Following Non-Cardiac Surgery Using Open Datasets.Journal of personalized medicine · 2024Article
- Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy.PloS one · 2024Article
- Characterizing the temporal changes in association between modifiable risk factors and acute kidney injury with multi-view analysis.International journal of medical informatics · 2022Article
- Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.JAMA network open · 2022Article
- Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on Information Theory and Historical Conditional Probability: Observational Study.JMIR medical informatics · 2022Article
- Machine learning model for predicting acute kidney injury progression in critically ill patients.BMC medical informatics and decision making · 2022Article
- Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study.BMC nephrology · 2020Article
- A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population.Frontiers in artificial intelligence · 2020Article
- Risk Stratification for Postoperative Acute Kidney Injury in Major Noncardiac Surgery Using Preoperative and Intraoperative Data.JAMA network open · 2019Article
- Using Electronic Health Record Activity to Represent Interdisciplinary Care Teams and Examining their Contribution to Hospital Length of Stay.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
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9 authors.
Funding
Abstract
Acute Kidney Injury (AKI) is a common complication encountered among hospitalized patients, imposing significantly increased cost, morbidity, and mortality. Early prediction of AKI has profound clinical implications because currently no treatment exists for AKI once it develops. Feature selection (FS) is an essential process for building accurate and interpretable prediction models, but to our best knowledge no study has investigated the robustness and applicability of such selection process for AKI. In this study, we compared eight widely-applied FS methods for AKI prediction using nine-years of electronic medical records (EMR) and examined heterogeneity in feature rankings produced by the methods. FS methods were compared in terms of stability with respect to data sampling variation, similarity between selection results, and AKI prediction performance. Prediction accuracy did not intrinsically guarantee the feature ranking stability. Across different FS methods, the prediction performance did not change significantly, while the importance rankings of features were quite different. A positive correlation was observed between the complexity of suitable FS method and sample size. This study provides several practical implications, including recognizing the importance of feature stability as it is desirable for model reproducibility, identifying important AKI risk factors for further investigation, and facilitating early prediction of AKI.
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