MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data ...
HybridLeg robots Olaf and Snogie use impact-safe design and self-recovery to enable scalable, real-world hardware ...
Researchers developed a machine learning model that could identify children in the ED who were at risk for developing sepsis ...
A machine learning lung cancer risk prediction model outperformed logistic regression, supporting improved risk assessment and more efficient radiology based lung cancer screening.
Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
ABSTRACT: Heart disease remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually. Early detection of individuals at risk is essential for reducing ...
1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...
1 Department of Science and Education, Shenyang Maternity and Child Health Hospital, Shenyang, China 2 Department of Maternal, Child and Adolescent Health, School of Public Health, Shenyang Medical ...
Background This study aimed to develop and validate a hybrid model integrating clinical features, vessel wall magnetic resonance imaging (VWMRI) characteristics, and radiomic features to predict the ...