Predicting the Clock: Machine Learning Refines Insulin Timing in Gestational Diabetes Management

Predicting the Clock: Machine Learning Refines Insulin Timing in Gestational Diabetes Management

A secondary analysis of the EMERGE trial reveals that Random Survival Forest models utilizing early glycemic data can accurately predict the time to insulin initiation in women with gestational diabetes, offering a roadmap for personalized metabolic management and improved maternal-fetal outcomes.
Predicting Post-Hepatectomy Liver Failure with the PILOT Architecture: Integrating Liver Regeneration Biomarkers and Time-Phased Machine Learning

Predicting Post-Hepatectomy Liver Failure with the PILOT Architecture: Integrating Liver Regeneration Biomarkers and Time-Phased Machine Learning

The novel PILOT machine learning architecture integrates time-phased perioperative data and regeneration-associated biomarkers to predict post-hepatectomy liver failure within six hours of surgery, significantly outperforming traditional clinical models and enabling early personalized risk stratification.