EPPC-OASIS combines ontology-aware fine-tuning via Wasserstein alignment with structured inference refinement to extract EPPC codes from secure messages, reporting 77.13% Code+Sub-code F1 and 63.83% Triplet F1 with small gains over supervised fine-tuning baselines.
EPPCMinerBen: A novel benchmark for evaluating large language models on electronic patient-provider communica- tion via the patient portal
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EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages
EPPC-OASIS combines ontology-aware fine-tuning via Wasserstein alignment with structured inference refinement to extract EPPC codes from secure messages, reporting 77.13% Code+Sub-code F1 and 63.83% Triplet F1 with small gains over supervised fine-tuning baselines.