EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages
The paper presents EPPC-OASIS, a framework designed for mining electronic patient-provider communication in secure messages. It focuses on ontology-aware adaptation and structured inference refinement to improve the extraction of clinically relevant communication behaviors. The results indicate that this approach can enhance the accuracy of automated extraction, although further validation is necessary before practical application.
- ▪EPPC-OASIS aims to automate the extraction of communication behaviors from secure patient-provider messages.
- ▪The framework combines ontology-aware adaptation with inference refinement to improve annotation coherence.
- ▪Evaluation showed that the best pipeline achieved a Code+Sub-code F1 score of 77.13%, indicating significant improvements over existing methods.
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Computer Science > Artificial Intelligence arXiv:2605.24172 (cs) [Submitted on 22 May 2026] Title:EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages Authors:Samah Fodeh, Sreeraj Ramachandran, Elyas Irankhah, Muhammad Arif, Afshan Khan, Ganesh Puthiaraju, Linhai Ma, Srivani Talakokkul, Jordan Alpert, Sarah Schellhorn View a PDF of the paper titled EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages, by Samah Fodeh and 9 other authors View PDF HTML (experimental) Abstract:Secure patient-provider messages contain clinically important communication behaviors that are difficult to characterize manually at…
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