A Correspondence Score for Validating Learned Patient Representations in Clinical Tabular Data

[2608.27489] A Correspondence Score for Validating Learned Patient Representations in Clinical Tabular Data Skip to main content Search Submit Donate Log in Quantitative Biology > Quantitative Methods arXiv:2608.27489 (q-bio) [Submitted on 26 Aug 2026] Title: A Correspondence Score for Validating Learned Patient Representations in Clinical Tabular Data Authors: Majid Lotfian Delouee , Sjors G. J. G. In 't Veld , Martijn C. Schut View PDF HTML (experimental) Abstract: Identifying patients with similar clinical characteristics is important for cohort discovery, clinical decision support, and personalized medicine. However, it is unclear whether learned patient representations preserve the similarities present in the original clinical data. We propose a correspondence score to directly assess how well these relationships are preserved, independently of labeled outcomes. The score combines complementary relationship-preservation metrics, with dataset-specific weights estimated through Bayesian optimization. We evaluated the approach on four complete blood count datasets using representations generated by five tabular foundation models and six classical transformation methods. Correspondence differed substantially across methods and representation dimensionalities. Patient relationships were generally best preserved at intermediate dimensionalities, while stronger reduction or expansion introduced greater deviations from the original data structure. Foundation models preserved correspondence more consistently than classical methods, with improvements ranging from approximately 5% to over 125%. The proposed score provides a way to evaluate representation quality before downstream use and supports informed selection of representation methods and dimensionalities. Subjects: Quantitative Methods (q-bio.QM) Cite as: arXiv:2608.27489 [q-bio.QM] (or arXiv:2608.27489v1 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2608.27489 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Majid Lotfian Delouee [ view email ] [v1] Wed, 26 Aug 2026 10:33:28 UTC (6,245 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.QM < prev | next > new | recent | 2026-08 Change to browse by: q-bio References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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