ArticleiScience2026
Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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Who cites it
2 citing papers in PubMed.
- Explainable machine learning and physics-constrained optimization of chalcogen catalysts for sustainable hydrogen production.iScience · 2026Article
- Progression of early diagnostic markers for diabetic kidney disease: From single-indicator detection to multi-omics integration modeling.Current research in physiology · 2026Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prognostic assessment of diabetic kidney disease (DKD) is essential for personalized management. This study developed eight machine learning models using data from 180 biopsy-proven patients with DKD to predict a composite endpoint of all-cause mortality, dialysis initiation, or renal transplantation. Internally, the Naive Bayes (NB) model achieved the highest accuracy of 82.3%, while the logistic regression (LR), support vector machine (SVM), and NB models shared the highest AUC of 0.788. An independent external validation confirmed robust generalizability, yielding an AUC of 0.834. SHAP analysis identified eGFR, serum albumin, C3, serum creatinine, and urinary red blood cell count (URBC) as the most impactful features. Feature stability was confirmed via a "leave-top1-out" sensitivity analysis. The models highlighted the predictive value of C3 and URBC by capturing non-linear patterns often missed by traditional linear methods, providing granular insights for personalized prognosis evaluation.
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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.