ArticleJournal of the American Medical Informatics Association : JAMIA2024
BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Effects of Model Choice, Corpus Context, and Post Hoc Correction on Layer-Level Embedding Degradation in Clinical Document Retrieval: Experimental Study.JMIR medical informatics · 2026Article
- Disparate language and model effects on AI-based translation and recognition of genetic conditions.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- CellExLink: End-to-end cell-type recognition and normalization in biomedical text.PLoS computational biology · 2026Article
- Clinical Context Variables Collectively Rival Model Choice in Embedding-Based Retrieval: Multi-Corpus Benchmark Study.JMIR medical informatics · 2026Article
- OpenVocabCT: Toward Universal Text-Driven CT Image Segmentation.IEEE transactions on medical imaging · 2026Article
- End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT.Scientific reports · 2026Article
- Bridging data and discovery: a survey on knowledge graphs in AI for science.National science review · 2026Review
- Efficient Drug Terminology Mapping with Bidirectional Late-Interaction Reranking and Deterministic Reordering.Healthcare informatics research · 2026Article
- Using semantic search to find publicly available gene-expression datasets.Bioinformatics (Oxford, England) · 2026Article
- Phecoder: semantic retrieval for auditing and expanding ICD-based phenotypes in EHR biobanks.medRxiv : the preprint server for health sciences · 2026Article
- ArcMAP - ML assisted medical concept mapping to accelerate NHS data standardization.Frontiers in digital health · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- SimSUM - simulated benchmark with structured and unstructured medical records.Journal of biomedical semantics · 2025Article
- A scalable framework for benchmark embedding models in semantic health-care tasks.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems.Scientific reports · 2025Article
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.BMC medical informatics and decision making · 2025Article
- Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records: Feasibility and Validation Study.Journal of medical Internet research · 2025Article
- Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A natural language processing approach to support biomedical data harmonization: Leveraging large language models.PloS one · 2025Article
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3 authors.
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Abstract
objectiveIn this study, we investigate the potential of large language models (LLMs) to complement biomedical knowledge graphs in the training of semantic models for the biomedical and clinical domains. MATERIALS AND
methodsDrawing on the wealth of the Unified Medical Language System knowledge graph and harnessing cutting-edge LLMs, we propose a new state-of-the-art approach for obtaining high-fidelity representations of biomedical concepts and sentences, consisting of 3 steps: an improved contrastive learning phase, a novel self-distillation phase, and a weight averaging phase.
resultsThrough rigorous evaluations of diverse downstream tasks, we demonstrate consistent and substantial improvements over the previous state of the art for semantic textual similarity (STS), biomedical concept representation (BCR), and clinically named entity linking, across 15+ datasets. Besides our new state-of-the-art biomedical model for English, we also distill and release a multilingual model compatible with 50+ languages and finetuned on 7 European languages. DISCUSSION: Many clinical pipelines can benefit from our latest models. Our new multilingual model enables a range of languages to benefit from our advancements in biomedical semantic representation learning, opening a new avenue for bioinformatics researchers around the world. As a result, we hope to see BioLORD-2023 becoming a precious tool for future biomedical applications.
conclusionIn this article, we introduced BioLORD-2023, a state-of-the-art model for STS and BCR designed for the clinical domain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.