Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction

arXiv:2404.14815v1 Announce Type: new
Abstract: The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods. These methods typically require extensive data for training due to their large parameter sets. However, existing works do not exploit the full potential of EHR data. A significant challenge arises from the infrequent occurrence of many medical codes within EHR data, limiting their clinical applicability. Current research often lacks …

abstract application arxiv attention cs.lg data deep learning disease ehr electronic electronic health records event graph health however medical medical field merging prediction records risk training transformer type