A machine-learning model utilizing six routine perioperative clinical variables accurately predicts intestinal failure-associated liver disease in infants following enterostomy for ...
Researchers evaluated machine learning models predicting depression risk among middle-aged and older adults across stratified air-quality levels, identifying distinct feature ...
Researchers integrated explainable machine learning and 3D gravity inversion to delineate copper prospectivity corridors and identify new exploration targets in ...
An explainable machine learning model reliably predicts two-month treatment responses in melanoma brain metastases treated with stereotactic body radiation therapy.
While machine learning models can accurately interpolate drug solubility in supercritical carbon dioxide for represented compounds, they fail to generalize ...
Researchers developed machine learning models integrating compartment-specific TGFBIp expression with clinicopathological features to predict vital status and recurrence in epithelial ...
A partitioned feature selection framework improves the interpretability and predictive performance of questionnaire-based depression severity models by grouping interrelated symptoms ...
An attention-enhanced ResNet-50 deep learning model achieved an accuracy of 0.833 and an area under the curve of 0.855 when ...
An investigation of Bangladesh Demographic and Health Survey data shows that higher cumulative fertility and smaller household sizes strongly predict ...
An interpretable CatBoost model integrating ctDNA methylation markers and clinical features accurately distinguished hepatocellular carcinoma from non-malignant nodules in hepatitis ...
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