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Federated Learning-Based Model for Predicting Mortality: Systematic Review and Meta-Analysis

Federated Learning-Based Model for Predicting Mortality: Systematic Review and Meta-Analysis

Li et al [22] incorporated 10 simulated sites from a tertiary hospital in Singapore by implementing a scoring-based system (the Fed Score model) to facilitate cross-institutional collaborations to predict mortality within 30 days after ED visits. Similarly, FL models outperformed CML in predicting the mortality of hospitalized patients with COVID-19 and pulmonary thromboendarterectomy using a real-world dataset [26,27].

Nurfaidah Tahir, Chau-Ren Jung, Shin-Da Lee, Nur Azizah, Wen-Chao Ho, Tsai-Chung Li

J Med Internet Res 2025;27:e65708

A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation

A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation

For instance, Li et al’s [11] network analysis revealed that the leptin biomarker indicated low levels of energy metabolism in patients with cold syndrome, while the human monocyte chemoattractant protein-1 (CCL2/MCP1) biomarker suggested intensified immune regulation in patients with hot syndrome, based on a study involving patients with chronic superficial gastritis and chronic atrophic gastritis.

Xiaojie Jin, Yanru Wang, Jiarui Wang, Qian Gao, Yuhan Huang, Lingyu Shao, Jiali Zhao, Jintian Li, Ling Li, Zhiming Zhang, Shuyan Li, Yongqi Liu

JMIR Med Inform 2025;13:e64725

Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis

Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis

Furthermore, we evaluated the follicular TI-RADS scoring criteria developed by Li et al [34] with our data and found a lower sensitivity (0.044 with a threshold for FTC risk set at >90%, and 0.269 with using a >50% FTC risk threshold) in evaluating the prediction model. These inconsistencies and the reduced sensitivity observed in external datasets may, at least in part, be attributed to the limitation of not considering tumor size when predicting the malignancy risk.

Xin Li, Wen-yu Yang, Fan Zhang, Rui Shan, Fang Mei, Shi-Bing Song, Bang-Kai Sun, Jing Chen, Run-ze Hu, Yang Yang, Yi-hang Yang, Jing-yao Liu, Chun-Hui Yuan, Zheng Liu

JMIR Cancer 2025;11:e73069