Patient Representation

Patient representation in healthcare focuses on creating comprehensive and accurate digital summaries of individual patient health information to improve clinical outcomes and facilitate research. Current research emphasizes developing sophisticated models, including graph neural networks, transformers, and recurrent neural networks, to integrate diverse data sources (e.g., electronic health records, medical images, patient-reported outcomes) into robust patient representations that capture temporal dynamics and complex interrelationships between clinical variables. These advancements aim to enhance the accuracy of predictive models for various clinical tasks, such as disease prediction, risk stratification, and personalized treatment recommendations, ultimately leading to improved patient care and more efficient healthcare systems.