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Zheng Yin

As seen in: British Ecological Society (Wiley), Color Research & Application, Nature, Wiley, Wiley Online Library, PLOS, Frontiers, MDPI, Sexually Transmitted Infections, ACS Publications and
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Articles

CAMI-DM: Development and validation of a multi-algorithm model for in-hospital mortality risk prediction in diabetic patients with acute myocardial infarction — The China acute myocardial infarction registry

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By Zheng Yin, Junxing Lv, Zuoxiang Wang
| PLOS
Loading metrics Open Access Peer-reviewed Research Article Citation: Wang Z, Yin Z, Lv J, Zhao S, Ba Z, Yang J, et al. (2026) CAMI-DM: Development and validation of a multi-algorithm model for in-hospital mortality risk prediction in diabetic patients with acute myocardial infarction — The China acute myocardial infarction registry. PLOS Digit Health 5(8): e0001529.
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Development and validation of a predictive model for diabetic kidney disease risk in patients with T2DM: a hospital data platform study

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By Zhanlin Zhang, Tianrong Guo, Yu Zhao, Zheng Yin, Gangyi Wang, GuoXia Ma, MeiFang Ma, XiuSheng Cheng, Zhonglin Yan, YongDong An
| Frontiers
Abstract Objective: This study used clinical data from patients with type 2 diabetes mellitus (T2DM) and applied least absolute shrinkage and selection operator (LASSO) regression to identify risk factors for diabetic kidney disease (DKD). We then constructed a nomogram prediction model to support early clinical screening of high-risk populations. Methods: Clinical data from patients with T2DM were collected from January 2020 to December 2025.
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Prediction of Carbon Dioxide Adsorption Performance of Covalent Organic Frameworks (COFs) Based on Machine Learning: Identification of Key Factors and Model Deployment Click to copy article link Article link copied!

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By Zheng Yin, Zhiyang Jiang, Zhongke Jiang
| ACS Publications
3.1. Training and Construction of Machine Learning Models This study employed six machine learning models─XGBoost (XGB), Random Forest (RF), k -Nearest Neighbors (KNN), CatBoost, Gradient Boosting Decision Trees (GBDT), and Bagging─to predict the carbon dioxide adsorption capacity. All models were optimized via 5-fold cross-validation (5-fold CV) to mitigate overfitting. Table S3 lists the R2 , MAE, and RMSE for each fold of the 5-fold cross-validation for all six models.
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