AI Model Flags Which Mild Stroke Patients Won’t Fully Recover After Clot-Busting Drugs
Researchers in China built an explainable XGBoost model using six routine admission variables to predict which mild stroke patients will ...
Researchers in China built an explainable XGBoost model using six routine admission variables to predict which mild stroke patients will ...
A lean XAI framework combining SHAP, SHAPTime, and rolling-origin validation delivers stable, horizon-aware explanations for supply-chain demand forecasts on the ...
An interpretable machine-learning study shows that four routine clinical and PET/CT features can predict lymph node metastasis in lung cancer ...
Researchers used SHAP-based explainable AI to identify a single EEG electrode and just seven features that detect virtual reality motion ...
A machine learning study shows that waste marble dust can replace up to 32 percent of the cement in semi-flexible ...
A new study combines hydrological simulation, machine learning and spatial statistics to rank flood risk for 5,482 historic buildings across ...
An explainable machine learning study of PISA 2022 data finds that digital learning capacity, mathematics anxiety, and their interaction best ...
A Chinese research team combined delta-radiomics from CT scans with clinical data and SHAP-based machine learning interpretation to predict the ...
Researchers in China have developed an interpretable machine learning model that analyzes all liver metastases on contrast-enhanced CT scans to ...
Researchers in China have built an explainable deep learning model that analyzes contrast-enhanced CT scans to predict whether non-muscle-invasive bladder ...
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© 2026 Scienmag - Science Magazine