Free Text Clinical

Free-text clinical data analysis focuses on extracting meaningful information from unstructured clinical notes, aiming to improve healthcare efficiency and research. Current research heavily utilizes large language models (LLMs), such as BERT and GPT variants, often combined with rule-based methods and other deep learning architectures, to perform tasks like named entity recognition, concept normalization, and de-identification. These advancements enable automated medical coding, improved clinical decision support (e.g., for triage and medication management), and the extraction of crucial information like social determinants of health, all while addressing privacy concerns through robust de-identification techniques. The ultimate goal is to unlock the vast potential of this rich data source for both clinical practice and biomedical research.