ATHENA: Knowledge-guided agentic neural architecture search for AutoFormer-based electronic health record modeling
arXiv:2608.21712v1 Announce Type: new Abstract: Transformer-based models are widely used for clinical prediction from electronic health records (EHRs), yet their architectures still require substantial manual tuning, and the optimal configuration may vary across tasks and hospitals. Neural architecture search (NAS) aut
Teams affected by ATHENA need to decide whether its documented change alters their current workflow. For researchers and builders, this is an early signal to fold into evaluation, model choice, or agent design.