Emergency care outcome prediction

This research develops machine learning models capable of rapidly stratifying patient risk for major adverse cardiac events (MACE) directly within emergency departments. By integrating multimodal clinical data, including ECG results and patient history, the goal is to create predictive tools that enhance diagnostic accuracy, ultimately supporting accelerated safe discharge or prioritizing immediate critical care.