{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T04:30:53Z","timestamp":1786336253900,"version":"build-2736575974"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To propose a dynamic feature-enhanced TabTransformer framework that providing a more effective and accurate tool for predicting Contrast-Induced Nephropathy (CIN).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study proposes CIN-RiskNet, a dynamic feature-enhanced TabTransformer model integrated with a hybrid SMOTE-Noise augmentation strategy. The approach includes adaptive feature gating to suppress noise, synthetic minority oversampling to address class imbalance, and multi-head self-attention to capture complex feature interactions. The model was trained and evaluated under a leakage-free stratified five-fold cross-validation protocol, where SMOTE and Gaussian noise were applied only to the training split within each fold trained on a clinical dataset from Tianjin University Chest Hospital that including a total of 1,679 patients who underwent percutaneous coronary intervention for coronary heart disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Under leakage-free five-fold evaluation, CIN-RiskNet achieved strong performance with an accuracy of 95.40%, a recall of 95.40%, and an F1-score of 95.42%. It attained the highest F1-score and recall among all evaluated configurations. It outperformed not only traditional machine learning models including XGBoost, Random Forest, and Support Vector Machine, but also the Mehran risk score, a widely used clinical scoring system for CIN prediction. Ablation studies confirmed the contributions of each module, demonstrating improved robustness and generalization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed model effectively addresses key challenges in CIN prediction, including class imbalance and feature noise, through an integrated deep learning framework. It shows promising potential as a decision-support tool, while external multicenter validation remains necessary before broad clinical deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1909097","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T04:21:37Z","timestamp":1786335697000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CIN-RiskNet: a dynamic feature-enhanced TabTransformer with hybrid SMOTE-noise augmentation for contrast-induced nephropathy prediction"],"prefix":"10.3389","volume":"13","author":[{"given":"Peng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Cardiology, Chest Hospital, Tianjin University","place":["Tianjin, China"]},{"name":"Tianjin Key Laboratory of Cardiovascular Emergency and Critical Care, Tianjin Municipal Science and Technology Bureau","place":["Tianjin, 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