Ontology- and LLM-based data harmonization for federated learning in healthcare

Abstract

The proposed pipeline transforms ontology-based harmonization from a manual expert task into a reusable and configurable workflow suitable for federated healthcare research. By combining high-recall retrieval with LLM-based semantic adjudication, the approach enables scalable, privacy-preserving conversion of heterogeneous clinical text into standardized representations across domains.

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