A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records

arXiv:2311.11483v2 Announce Type: replace
Abstract: Foundation models hold promise for transforming AI in healthcare by providing modular components that are easily adaptable to downstream healthcare tasks, making AI development more scalable and cost-effective. Structured EHR foundation models, trained on coded medical records from millions of patients, demonstrated benefits including increased performance with fewer training labels, and improved robustness to distribution shifts. However, questions remain on the feasibility of sharing these models across different hospitals and their performance for local task …

abstract adaptability ai development arxiv center components cost cs.ai cs.lg development ehr electronic electronic health records foundation foundation model health healthcare making medical medical records modular records scalable study tasks type