Enhancing Medication Safety with LLMs

Medication safety poses a significant challenge in healthcare, with adverse reactions harming patients and increasing costs. We present two solutions based on Large Language Models (LLMs): HELIOT, a Clinical Decision Support System for managing adverse drug reactions, and an approach that uses textual-drug information to predict drug-drug interactions (DDIs). HELIOT examines patient-specific clinical narratives to provide contextual alerts, reducing alert fatigue by over 50%. Our DDI system analyzes molecular structures, organisms, and drug target genes, achieving a sensitivity of 0.978 and an accuracy of 0.919 across 13 validation datasets, with smaller LLMs (2-3 billion parameters) outperforming larger ones. Both systems demonstrate the potential of LLMs to enhance medication safety through advanced language processing and pharmaceutical data interpretation. Future work will focus on practical validation and integration into healthcare systems.