Researchers have developed ATHENA, a novel knowledge-guided agentic neural architecture search (NAS) framework specifically designed for Transformer-based electronic health record (EHR) modeling. This system aims to automate the optimization of model architectures, which typically require extensive manual tuning for clinical prediction tasks. ATHENA incorporates a weight-sharing supernet and a cross-hospital architecture prior, leveraging past successful designs and component effects to guide a multi-agent LLM search. Across six clinical prediction tasks and two health systems, ATHENA demonstrated competitive or superior performance compared to four NAS baselines within a limited search budget. AI
IMPACT Streamlines the development of specialized AI models for healthcare, potentially improving clinical prediction accuracy and efficiency.
RANK_REASON The cluster contains an academic paper detailing a new method for neural architecture search. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.MA (Multiagent) →
- AutoFormer
- electronic health records
- Hugging Face
- large language model
- neural architecture search
- SHapley Additive exPlanations
- Transformer
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