EHR Datasets

Electronic health record (EHR) datasets are a rich source of patient information crucial for clinical research and practice, driving efforts to develop robust and fair predictive models for various clinical tasks. Current research focuses on addressing challenges like data heterogeneity, missing values, and imbalanced datasets through techniques such as multimodal fusion (integrating structured and unstructured data), contrastive learning (improving feature representation), and retrieval augmentation (incorporating external knowledge sources). These advancements aim to improve the accuracy, fairness, and reproducibility of machine learning models built upon EHR data, ultimately leading to better clinical decision-making and more efficient healthcare.