{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T13:13:00Z","timestamp":1778850780001,"version":"3.51.4"},"posted":{"date-parts":[[2026,5,13]]},"group-title":"Health Informatics","reference-count":41,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>The significant challenges associated with rare diseases in the medical and research domains include the scarcity of information, which is often confined to unstructured formats. Although existing approaches provide valuable insights, there is a need to develop effective methods to identify information pertinent to rare diseases for advancing rare disease research. We identified mentions of rare diseases in relevant texts and assessed their relevance using derived scores, the confidence score and semantic similarity from a fine-tuned BioMedBERT encoder. This encoder was fine-tuned using rare disease related text from Online Mendelian Inheritance in Man (OMIM), Orphanet, a manually validated dataset, and STS benchmark datasets. The process of identifying meaningful rare disease mentioned was presented through two case studies that retrieved relevant NIH-funded projects, utilizing a generated knowledge graph in Neo4j to host data on 2,067 GARD diseases with over 320,000 NIH funded projects. Through various case studies with NIH-funded projects related to rare diseases, we demonstrated the effectiveness of our approach in systematically providing rare disease related data to enhance our understanding of rare diseases for future investigations.<\/jats:p>","DOI":"10.64898\/2026.05.11.26352910","type":"posted-content","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T22:55:16Z","timestamp":1778712916000},"source":"Crossref","is-referenced-by-count":0,"title":["Synonym Augmentation for Rare Disease Identification in Unstructured Data"],"prefix":"10.64898","author":[{"given":"Jaber","family":"Valinejad","sequence":"first","affiliation":[{"name":"Division of Preclinical Innovations, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungrim","family":"Moon","sequence":"additional","affiliation":[{"name":"Division of Preclinical Innovations, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanji","family":"Xu","sequence":"additional","affiliation":[{"name":"Division of Rare Disease Research Innovations, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zhu","sequence":"additional","affiliation":[{"name":"Division of Preclinical Innovations, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026051506000583000_2026.05.11.26352910v1.1","doi-asserted-by":"crossref","unstructured":"Gordon, L.B. , W.T. Brown , and F.S. Collins , Hutchinson-Gilford progeria syndrome. 2019.","DOI":"10.1016\/j.bone.2019.05.008"},{"issue":"9629","key":"2026051506000583000_2026.05.11.26352910v1.2","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1016\/S0140-6736(08)60872-7","article-title":"Why rare diseases are an important medical and social issue","volume":"371","year":"2008","journal-title":"The Lancet"},{"key":"2026051506000583000_2026.05.11.26352910v1.3","doi-asserted-by":"crossref","unstructured":"Sosa, D.N. , et al. A literature-based knowledge graph embedding method for identifying drug repurposing opportunities in rare diseases. in Pacific Symposium on Biocomputing 2020. 2019. World Scientific.","DOI":"10.1101\/727925"},{"key":"2026051506000583000_2026.05.11.26352910v1.4","first-page":"1","article-title":"Scientific evidence based rare disease research discovery with research funding data in knowledge graph","volume":"16","year":"2021","journal-title":"Orphanet Journal of Rare Diseases"},{"issue":"1","key":"2026051506000583000_2026.05.11.26352910v1.5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.5582\/irdr.2023.01111","article-title":"The use of artificial intelligence in the treatment of rare diseases: A scoping review","volume":"13","year":"2024","journal-title":"Intractable & Rare Diseases Research"},{"key":"2026051506000583000_2026.05.11.26352910v1.6","doi-asserted-by":"crossref","first-page":"103961","DOI":"10.1016\/j.jbi.2021.103961","article-title":"The RareDis corpus: a corpus annotated with rare diseases, their signs and symptoms","volume":"125","year":"2022","journal-title":"Journal of Biomedical Informatics"},{"key":"2026051506000583000_2026.05.11.26352910v1.7","doi-asserted-by":"publisher","DOI":"10.1093\/database\/bau045"},{"key":"2026051506000583000_2026.05.11.26352910v1.8","first-page":"1","article-title":"Rare disease knowledge enrichment through a data-driven approach","volume":"19","year":"2019","journal-title":"BMC medical informatics and decision making"},{"issue":"1","key":"2026051506000583000_2026.05.11.26352910v1.9","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1186\/s12859-022-04810-y","article-title":"Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts","volume":"23","year":"2022","journal-title":"BMC bioinformatics"},{"key":"2026051506000583000_2026.05.11.26352910v1.10","doi-asserted-by":"crossref","unstructured":"Dong, H. , et al. Rare disease identification from clinical notes with ontologies and weak supervision. in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. IEEE.","DOI":"10.1109\/EMBC46164.2021.9630043"},{"key":"2026051506000583000_2026.05.11.26352910v1.11","unstructured":"Shyr, C. , et al., Identifying and Extracting Rare Diseases and Their Phenotypes with Large Language Models. Journal of Healthcare Informatics Research, 2024: p. 1\u201324."},{"key":"2026051506000583000_2026.05.11.26352910v1.12","doi-asserted-by":"crossref","unstructured":"Pokhrel, K. , et al. OrphaGPT: An Adapted Large Language Model for Orphan Diseases Classification. in Asian Conference on Intelligent Information and Database Systems. 2024. Springer.","DOI":"10.1007\/978-981-97-4982-9_16"},{"key":"2026051506000583000_2026.05.11.26352910v1.13","doi-asserted-by":"crossref","unstructured":"Wang, A. , et al., Fine-tuning large language models for rare disease concept normalization. Journal of the American Medical Informatics Association, 2024: p. ocae133.","DOI":"10.1101\/2023.12.28.573586"},{"key":"2026051506000583000_2026.05.11.26352910v1.14","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkh061"},{"key":"2026051506000583000_2026.05.11.26352910v1.15","unstructured":"Orphanet. Knowledge on rare diseases and orphan drugs. Available from: https:\/\/www.orpha.net\/."},{"key":"2026051506000583000_2026.05.11.26352910v1.16","doi-asserted-by":"crossref","unstructured":"Hsu, S. , et al. Semantic Annotation of NIH Funding Data for Supporting Rare Disease Research. in 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2022. IEEE.","DOI":"10.1109\/BIBM55620.2022.9994880"},{"key":"2026051506000583000_2026.05.11.26352910v1.17","unstructured":"GARD. National Center for Advancing Translational Sciences. Available from: https:\/\/rarediseases.info.nih.gov\/."},{"key":"2026051506000583000_2026.05.11.26352910v1.18","doi-asserted-by":"publisher","DOI":"10.1186\/s13326-019-0218-0"},{"issue":"6","key":"2026051506000583000_2026.05.11.26352910v1.19","first-page":"1930","article-title":"A comparative study of stemming algorithms","volume":"2","year":"2011","journal-title":"Int. J. Comp. Tech. Appl"},{"issue":"2","key":"2026051506000583000_2026.05.11.26352910v1.20","first-page":"794","article-title":"Fuzzy bag-of-words model for document representation","volume":"26","year":"2017","journal-title":"IEEE transactions on fuzzy systems"},{"key":"2026051506000583000_2026.05.11.26352910v1.21","unstructured":"RePORTER. NIH RePORTER. Available from: https:\/\/reporter.nih.gov\/."},{"key":"2026051506000583000_2026.05.11.26352910v1.22","unstructured":"ExPORTER. NIH ExPORTER. Available from: https:\/\/reporter.nih.gov\/exporter."},{"key":"2026051506000583000_2026.05.11.26352910v1.23","unstructured":"Vasiliev, Y. , Natural language processing with Python and spaCy: A practical introduction. 2020: No Starch Press."},{"key":"2026051506000583000_2026.05.11.26352910v1.24","unstructured":"BiomedBERT. Available from: https:\/\/huggingface.co\/microsoft\/BiomedNLP-BiomedBERT-base-uncased-abstract."},{"key":"2026051506000583000_2026.05.11.26352910v1.25","unstructured":"STS-Benchmark. Semantic Textual Similarity. Available from: https:\/\/paperswithcode.com\/dataset\/sts-benchmark."},{"issue":"4","key":"2026051506000583000_2026.05.11.26352910v1.26","first-page":"1","article-title":"A new benchmark dataset with production methodology for short text semantic similarity algorithms","volume":"10","year":"2014","journal-title":"ACM Transactions on Speech and Language Processing (TSLP)"},{"key":"2026051506000583000_2026.05.11.26352910v1.27","unstructured":"OMIM. An Online Catalog of Human Genes and Genetic Disorders. Available from: https:\/\/www.omim.org\/."},{"key":"2026051506000583000_2026.05.11.26352910v1.28","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1708538"},{"key":"2026051506000583000_2026.05.11.26352910v1.29","unstructured":"FDA Orphan Drug Designations and Approvals Available from: https:\/\/www.accessdata.fda.gov\/scripts\/opdlisting\/oopd\/."},{"key":"2026051506000583000_2026.05.11.26352910v1.30","doi-asserted-by":"crossref","unstructured":"Hirschfield, G.M. , et al., Primary sclerosing cholangitis. The Lancet, 2013. 382(9904): p. 1587\u20131599.","DOI":"10.1016\/S0140-6736(13)60096-3"},{"key":"2026051506000583000_2026.05.11.26352910v1.31","unstructured":"Clinical Trial. Available from: ct.gov."},{"key":"2026051506000583000_2026.05.11.26352910v1.32","doi-asserted-by":"crossref","unstructured":"Herta, T. , et al., Odevixibat treatment in an adult patient with advanced icteric progressive cholestatic liver disease. JHEP Reports, 2024. 6(3).","DOI":"10.1016\/j.jhepr.2023.100978"},{"key":"2026051506000583000_2026.05.11.26352910v1.33","unstructured":"BlueBert. Available from: https:\/\/huggingface.co\/two-factor\/totp\/confirm?next=%2Fbionlp%2Fbluebert_pubmed_mimic_uncased_L-12_H-768_A-12."},{"key":"2026051506000583000_2026.05.11.26352910v1.34","unstructured":"BioMed-RoBERTa. Available from: https:\/\/huggingface.co\/allenai\/biomed_roberta_base."},{"key":"2026051506000583000_2026.05.11.26352910v1.35","unstructured":"biobert."},{"key":"2026051506000583000_2026.05.11.26352910v1.36","unstructured":"Bio_ClinicalBERT. Available from: https:\/\/huggingface.co\/emilyalsentzer\/Bio_ClinicalBERT."},{"key":"2026051506000583000_2026.05.11.26352910v1.37","doi-asserted-by":"crossref","unstructured":"Lim, Z.W. , et al., Benchmarking large language models\u2019 performances for myopia care: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Google Bard. EBioMedicine, 2023. 95.","DOI":"10.1016\/j.ebiom.2023.104770"},{"key":"2026051506000583000_2026.05.11.26352910v1.38","unstructured":"Stanford Medicine Funding Databases. Available from: https:\/\/med.stanford.edu\/rmg\/funding\/searchable-funding-databases.html."},{"key":"2026051506000583000_2026.05.11.26352910v1.39","unstructured":"Harvard University Funding Database. Available from: https:\/\/research.fas.harvard.edu\/funding-databases."},{"key":"2026051506000583000_2026.05.11.26352910v1.40","unstructured":"Duke Funding Database Available from: https:\/\/researchfunding.duke.edu\/."},{"key":"2026051506000583000_2026.05.11.26352910v1.41","unstructured":"CZI Community Fund. Available from: https:\/\/chanzuckerberg.com\/community\/fund\/."}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.05.11.26352910","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T13:00:46Z","timestamp":1778850046000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2026.05.11.26352910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":41,"URL":"https:\/\/doi.org\/10.64898\/2026.05.11.26352910","relation":{},"subject":[],"published":{"date-parts":[[2026,5,13]]},"subtype":"preprint"}}