Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma.

This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model.

Military and Civilian Trauma System Integration: A Global Case Series.

Introduction: Traumatic injury is a leading cause of morbidity globally, particularly in low-income and middle-income countries (LMICs). In high-income countries (HICs), it is well documented that military and civilian integration can positively impact trauma care in both healthcare systems, but it is unknown if this synergy could benefit LMICs. This case series examines the variety of integration between the civilian and military systems of various countries and international partnerships to elucidate if there are commonalities in facilitators and barriers.