Imagine a world where doctors could predict patient outcomes with greater accuracy, paving the way for more personalized and effective treatments. This isn't science fiction; it's the promise of EMERGE, a groundbreaking AI framework designed to revolutionize healthcare prediction. Electronic Health Records (EHRs), packed with valuable patient data, are often underutilized due to their complexity. EMERGE tackles this challenge by combining the power of large language models (LLMs) with the rich context of medical knowledge graphs. Think of it as giving the AI a medical textbook to reference while analyzing patient data. The system extracts key information from both structured data like lab results and unstructured data like clinical notes. It then uses this information to generate a concise summary of the patient's health status, which is then used to predict critical outcomes like in-hospital mortality and 30-day readmission. Tests on real-world datasets show that EMERGE outperforms existing models, demonstrating its potential to transform healthcare. One of the most exciting aspects of EMERGE is its ability to handle missing data, a common problem in EHRs. This robustness makes it particularly valuable in real-world clinical settings where complete data is often a luxury. While EMERGE represents a significant leap forward, challenges remain. Further research is needed to refine the system and ensure its responsible implementation. However, the potential benefits are undeniable. By unlocking the full potential of patient data, EMERGE offers a glimpse into a future where AI empowers doctors to make more informed decisions, ultimately leading to better patient care.