A machine learning lung cancer risk prediction model outperformed logistic regression, supporting improved risk assessment and more efficient radiology based lung cancer screening.

Step 1 was selecting the training cohort (n = 184) and the internal validation cohort (n = 62) by random seeds; Step 2 was selecting the optimal parameters through five times ten-fold cross-validation ...

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

A project at Rice University has developed a new machine learning (ML) algorithm intended to improve the identification of biomarkers in optical spectra. As reported in ACS Nano, the algorithm could ...