Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies.

ABSTRACT:

Objectives

Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sources. However, there are many parameters to optimize in just the retrieval system alone. This paper presents an ablation study exploring how different embedding models and pooling methods affect information retrieval for the clinical domain.

Materials and methods

Evaluating on 3 retrieval tasks on 2 electronic health record (EHR) data sources, we compared 7 models, including medical- and general-domain models, specialized encoder embedding models, and off-the-shelf decoder LLMs. We also examine the choice of embedding pooling str

SUBMITTER: Myers S 

PROVIDER: S-EPMC11756698 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature