---
title: "Benchmarking Biomedical Language Models for Medical Concept Normalization in Real-World Text"
id: "biorxiv-22-a-comparative-benchmark-of-biomedical-language-models-for-concept-normalization"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-22-a-comparative-benchmark-of-biomedical-language-models-for-concept-normalization"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1?rss=1"
published_at: "2026-09-21T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Benchmarking Biomedical Language Models for Medical Concept Normalization in Real-World Text
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-22-a-comparative-benchmark-of-biomedical-language-models-for-concept-normalization
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1?rss=1)
- **Published At:** 2026-09-21T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- This preprint systematically benchmarks transformer-based representation models and instruction-tuned LLMs for **medical concept normalization** (MCN) using real-world, heterogeneous short medical expressions. - The study evaluated 15 representation models (general, biomedical, clinical) via embedding-based semantic retrieval and 12 instruction-tuned LLMs as upstream text-correction modules. - Evaluation used 12,713 instances pooled from five established datasets: TAC2017_ADR, TwADR-L, TwiMed, CADEC and SMM4H2017. - Without correction, **SapBERT** achieved the highest Top-5 retrieval accuracy: 63.8% for **SNOMED CT** and 58.0% for **MedDRA**. - The authors compared instruction-tuned LLMs as automatic text correctors and identified **Qwen 2 Instruct** as the preferred corrector based on a balance of Top-1 normalization gains and computational efficiency versus much larger models such as Llama 3.1 Instruct (70B). - Incorporating Qwen 2 Instruct for text refinement raised Top-5 accuracy to 69.4% for SNOMED CT and 63.2% for MedDRA, demonstrating improved terminology mapping when correction is applied upstream of semantic retrieval. - The proposed scalable framework performs automatic text refinement, semantic retrieval and mapping to standardized vocabulary codes without manual pre-processing, accommodating informal and fragmented clinical and patient-reported language. - The benchmark and resulting framework provide a guide for selecting representation and instruction-tuned models for automated medical terminology standardization in downstream systems. - The article is a preprint and has not undergone peer review; competing interests were declared as none by the authors.
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Anshul Verma CSIR-Institute of Genomics and Integrative Biology * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Anshul%2BVerma%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Verma%20A&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AAnshul%2BVerma%2B) * [ORCID record for Anshul Verma](http://orcid.org/0009-0007-6686-3385 "Open in new tab") Abhijay CSIR-Institute of Genomics and Integrative Biology * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=%2BAbhijay%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Abhijay&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3A%2BAbhijay%2B) * [ORCID record for Abhijay](http://orcid.org/0009-0003-5381-442X "Open in new tab") Manan Vangani CSIR-Institute of Genomics and Integrative Biology * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Manan%2BVangani%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Vangani%20M&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AManan%2BVangani%2B) * [ORCID record for Manan Vangani](http://orcid.org/0009-0007-4772-0119 "Open in new tab") Satyartha Prakash CSIR-Institute of Genomics and Integrative Biology * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Satyartha%2BPrakash%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Prakash%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ASatyartha%2BPrakash%2B) * [ORCID record for Satyartha Prakash](http://orcid.org/0000-0002-8112-9222 "Open in new tab") Kumardeep Chaudhary CSIR-Institute of Genomics and Integrative Biology * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Kumardeep%2BChaudhary%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Chaudhary%20K&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AKumardeep%2BChaudhary%2B) * [ORCID record for Kumardeep Chaudhary](http://orcid.org/0000-0002-4117-6403 "Open in new tab") * For correspondence: kumardeep.igib@csir.res.in * [Abstract](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5799358/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5799358/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5799358/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5799358/1) * [Data/Code](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1.external-links)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_data_code/node:5799358/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5799358/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Patient-reported and clinically documented narratives often contain informal, fragmented and linguistically heterogeneous expressions, complicating medical concept normalization (MCN) and frequently necessitating pre-processing before terminology mapping. Despite rapid advances in biomedical language modeling, the comparative utility of representation models for MCN and their integration with instruction-tuned LLMs in automated normalization workflows remain underexplored. In this study, we systematically benchmarked 15 general, biomedical and clinical transformer-based representation models together with 12 instruction-tuned LLMs across 12,713 instances from five established datasets: TAC2017_ADR, TwADR-L, TwiMed, CADEC and SMM4H2017. Representation models were evaluated using embedding-based semantic retrieval, whereas instruction-tuned LLMs were assessed as upstream text-correction modules. SapBERT achieved the highest Top-5 accuracy among representation models, reaching 63.8% for SNOMED CT and 58.0% for MedDRA without correction. Qwen 2 Instruct was selected as the preferred corrector on the basis of its favorable balance between Top-1 normalization performance and computational efficiency relative to substantially larger models, including Llama 3.1 Instruct (70B). Incorporation of Qwen 2 Instruct increased Top-5 accuracy to 69.4% for SNOMED CT and 63.2% for MedDRA. The resulting framework accepts heterogeneous short medical expressions without manual input pre-processing and automatically performs text refinement, semantic retrieval and terminology mapping to standardized concepts and vocabulary codes. These findings establish a benchmark-guided, scalable framework for automated medical terminology standardization. ### Competing Interest Statement The authors have declared no competing interest. ## Footnotes * * ## Funder Information Declared Council of Scientific and Industrial Research, https://ror.org/021wm7p51, HCP47 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. [Donate to openRxiv ](https://www.zeffy.com/en-US/donation-form/donate-to-make-a-difference-10981) [ Back to top](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1?rss=1#page) [ Previous](https://www.biorxiv.org/content/10.64898/2026.09.20.751976v1 "Antibacterial and Anticancer Activities of Insulicolides from Streptomyces sp. UL09; An Endophyte of Ulva rigida C. Agardh")[Next ](https://www.biorxiv.org/content/10.64898/2026.09.17.752253v1 "Multiscale mapping of venous remodelling in idiopathic pulmonary fibrosis") Posted September 21, 2026. [ Download PDF](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/09/21/2026.09.19.752843.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [Supplementary Material ](https://www.biorxiv.org/content/10.64898/2026.09.19.752843v1.supplementary-material) [ Data/Code](https://www.biorxiv.org/content/early/2026/09/21/2026.09.19.752843.external-links) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) A Comparative Benchmark of Biomedical Language Models for Concept Normalization from Real-World Text Anshul Verma, Abhijay, Manan Vangani, Satyartha Prakash, Kumardeep Chaudhary bioRxiv 2026.09.19.752843; doi: https://doi.org/10.64898/2026.09.19.752843 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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