Disease Representation

Disease representation in healthcare research focuses on developing effective computational models to capture the complex relationships between diseases, patient characteristics, and clinical outcomes. Current efforts concentrate on leveraging diverse data sources (e.g., EHRs, radiology images, audio) and employing advanced architectures like graph neural networks, transformers, and large language models to learn rich, dynamic representations that account for temporal dependencies and heterogeneous information. These improved representations are crucial for enhancing the accuracy and interpretability of predictive models for tasks such as disease risk prediction, report generation, and medication recommendation, ultimately leading to more personalized and effective healthcare.