Clinical Natural Language Processing

Clinical Natural Language Processing (NLP) aims to extract meaningful information from unstructured clinical text data, improving healthcare efficiency and patient outcomes. Current research focuses on adapting large language models (LLMs) and simpler transformer-based architectures for tasks like assertion detection, concept extraction, and diagnostic reasoning, often employing techniques like prompt engineering and federated learning to address data scarcity and privacy concerns. These advancements are crucial for developing robust and reliable clinical decision support systems, enabling more accurate diagnoses, personalized treatments, and improved research capabilities.