An Extreme Gradient Boosting model using routinely collected bedside data accurately predicted delirium in non-mechanically ventilated sepsis patients during internal ...
A new machine learning model combining MRI radiomics and clinical data predicts stroke outcomes with higher accuracy than clinical or ...
A multimodal computed tomography framework combining habitat radiomics and Vision-Mamba deep learning outperformed conventional radiological assessment in preoperatively stratifying thymic ...
A comparative modeling study of critically ill patients with acute myocardial infarction demonstrated that early six-hour landmark mortality prediction is ...
A retrospective study of 669 breast cancer patients demonstrates that integrating intratumoral and peritumoral ultrasound habitat radiomics with clinical data ...
Integrating three-dimensional spatial topology features from baseline PET/CT with volumetric burden metrics significantly improves two-year progression-free survival risk stratification in ...
A retrospective cohort study of 4,498 singleton pregnancies found that a gradient boosting machine model using early clinical indicators accurately ...
A neural network model showed promising discrimination for predicting in-hospital mortality after surgery in male patients with acute type A ...
A dual-center study revealed the triglyceride-glucose index predicted new-onset cardiovascular events in elderly coronary heart disease patients more effectively than ...
A retrospective study shows that contrast-enhanced CT deep learning and habitat radiomics models effectively differentiate common pathological subtypes of small ...
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