Article

Prior Knowledge Shapes Fine-Tuning Success for Biomedical Term Normalization

2025-08-07

Abstract excerpt

Large language models (LLMs) often fail to correctly link biomedical terms to their standardized ontology identifiers, posing challenges for downstream applications that depend on accurate, machine-readable codes. These linking failures can compromise the integrity of data used in precision medicine, clinical decision support, and population health. Fine-tuning can partially remedy these issues, but the degree of...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 2ded4167-e9f2-5739-9640-c59d9cf33847
DOI: 10.20944/preprints202508.0574.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Prior Knowledge Shapes Fine-Tuning Success for Biomedical Term NormalizationDOI 10.20944/preprints202508.0574.v1Read

Fine-tuning large language models for rare disease concept normalization.Semantic proximity Read

Fine-tuning Large Language Models for Rare Disease Concept NormalizationSemantic proximity Read

Metadata Harmonization from Biological Datasets with Language ModelsSemantic proximity Read

<i>MetaHarmonizer</i> : robust biomedical metadata harmonization and a contamination control for inflated LLM performance on public benchmarksSemantic proximity Read

Large language models improve transferability of electronic health record-based predictions across countries and coding systemsSemantic proximity Read

Keyphrase Identification Using Minimal Labeled Data with Hierarchical Contexts and Transfer LearningSemantic proximity Read

Database-Augmented Transformer-Based Large Language Models Achieve High Accuracy in Mapping Gene-Phenotype RelationshipsSemantic proximity Read

An Empirical Analysis of Fine-Tuning Large Language Models on Bioinformatics Literature: PRSGPT and BioStarsGPTSemantic proximity Read

Towards understanding the disease landscape of clinical trials in Germany: Ontology and embedding-based pipelines versus Large Language Models for ICD-10 HarmonizationSemantic proximity Read

Large Language Models Struggle to Encode Medical Concepts — A Multilingual Benchmarking and Comparative AnalysisSemantic proximity Read

Application of large language models to the annotation of cell lines and mouse strains in genomics dataSemantic proximity Read

Biomedical Text Normalization through Generative ModelingSemantic proximity Read