ArticleJournal of the American Medical Informatics Association : JAMIA2019
Robust clinical marker identification for diabetic kidney disease with ensemble feature selection.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis.Endocrine · 2024Pooled it
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis.Systematic reviews · 2021Pooled it
- GERBEHRT: a BERT-based model tailored for German electronic health records - potential in chronic kidney disease prediction.BMC medical informatics and decision making · 2026Article
- Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning.iScience · 2026Article
- When complexity does not pay: benchmarking deep learning and ensemble methods for biomarker discovery.Briefings in bioinformatics · 2026Article
- Detection of Nondiabetic Kidney Disease in Patients with Diabetes Using Machine Learning and Electronic Medical Record Data.Kidney & blood pressure research · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification.PeerJ. Computer science · 2025Article
- From bytes to nephrons: AI's journey in diabetic kidney disease.Journal of nephrology · 2025Review
- RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning.BMC medical informatics and decision making · 2024Article
- A novel hyperparameter search approach for accuracy and simplicity in disease prediction risk scoring.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review.Journal of diabetes and metabolic disorders · 2024Article
- Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery.BMC bioinformatics · 2023Article
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
- Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods.BMC medical informatics and decision making · 2022Article
- Characterizing the temporal changes in association between modifiable risk factors and acute kidney injury with multi-view analysis.International journal of medical informatics · 2022Article
- New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients.Frontiers in endocrinology · 2022Observational
- Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment.International journal of environmental research and public health · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Objective: Diabetic kidney disease (DKD) is one of the most frequent complications in diabetes associated with substantial morbidity and mortality. To accelerate DKD risk factor discovery, we present an ensemble feature selection approach to identify a robust set of discriminant factors using electronic medical records (EMRs). Material and Methods: We identified a retrospective cohort of 15 645 adult patients with type 2 diabetes, excluding those with pre-existing kidney disease, and utilized all available clinical data types in modeling. We compared 3 machine-learning-based embedded feature selection methods in conjunction with 6 feature ensemble techniques for selecting top-ranked features in terms of robustness to data perturbations and predictability for DKD onset. Results: The gradient boosting machine (GBM) with weighted mean rank feature ensemble technique achieved the best performance with an AUC of 0.82 [95%-CI, 0.81-0.83] on internal validation and 0.71 [95%-CI, 0.68-0.73] on external temporal validation. The ensemble model identified a set of 440 features from 84 872 unique clinical features that are both predicative of DKD onset and robust against data perturbations, including 191 labs, 51 visit details (mainly vital signs), 39 medications, 34 orders, 30 diagnoses, and 95 other clinical features. Discussion: Many of the top-ranked features have not been included in the state-of-art DKD prediction models, but their relationships with kidney function have been suggested in existing literature. Conclusion: Our ensemble feature selection framework provides an option for identifying a robust and parsimonious feature set unbiasedly from EMR data, which effectively aids in knowledge discovery for DKD risk factors.
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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 Socratic graph.