Structured Clinical

Structured clinical data analysis focuses on extracting meaningful insights from diverse healthcare data sources, including electronic health records, clinical notes, and medical images, to improve patient care and clinical research. Current research emphasizes leveraging large language models (LLMs), graph neural networks (GNNs), and other deep learning architectures for tasks such as data standardization, information extraction, and predictive modeling, often incorporating techniques like retrieval-augmented generation and transfer learning to address data scarcity and heterogeneity. These advancements hold significant promise for enhancing clinical decision-making, accelerating drug discovery, and improving the efficiency and accuracy of healthcare workflows.