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This study aims to bridge that knowledge gap by utilizing the advancements of AI to interpret Electronic Medical Records (EMRs). Recently, Large Language Models (LLMs) have gained attention for their exceptional capabilities in understanding human language, which makes them crucial for AI systems in healthcare that focus on clinical narratives. In this study, we extracted 2,663 hospitalized adult patient records from UC Davis Medical Center archives between January 2010 and April 2020 to identify patients at high risk of OIRD. For this purpose, we employed clinical language models (BioBERT, ClinicalBERT, and GatorTron) and fine-tuned them on the OIRD dataset. 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