The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…The model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Shapley Additive exPlanations (SHAP) analysis was conducted to identify the most influential features contributing to mortality prediction.ResultsIn terms of AVGIB patients, extremely randomized trees model demonstrated excellent predictive value among other ML models, with the AUC of 0.996 ± 0.007, accuracy of 0.996 ± 0.009, precision of 0.957 ± 0.024, recall of 0.988 ± 0.012, and F1 score of 0.972 ± 0.007. …”
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Monitoring of greenhouse gas emission drivers in Atlantic Canadian Potato production: A robust explainable intelligent glass-box by Mehdi Jamei, Muhammad Hassan, Aitazaz A. Faroouqe, Mumtaz Ali, Masoud Karbasi, Gurjit S. Randhawa, Zaher Mundher Yaseen, Ross Dwyer

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Satellite Image Price Prediction Based on Machine Learning by Linhan Yang, Zugang Chen, Guoqing Li

“…AdaBoost also demonstrates competitive accuracy, while LightGBM and XGBoost exhibit larger errors in high-value regimes. SHapley Additive exPlanations (SHAP) analysis reveals that imaging mode and spatial resolution are the primary drivers of price variance across both domains, followed by satellite manufacturing cost and acquisition recency. …”
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