Multimodal Electronic Health Record

Multimodal electronic health records (EHRs) integrate diverse patient data, such as clinical notes, time series measurements, and images, to improve healthcare predictions and decision-making. Current research focuses on developing sophisticated fusion models, often employing techniques like graph neural networks, transformers (including those with attention mechanisms), and retrieval-augmented generation (RAG) frameworks incorporating large language models, to effectively combine these heterogeneous data types while addressing challenges like data sparsity and irregularity. These advancements aim to enhance the accuracy and interpretability of clinical predictions for various outcomes, ultimately leading to more personalized and effective patient care. Furthermore, there is growing emphasis on ensuring fairness and mitigating biases within these predictive models.