Leveraging EMR Data with AI to Forecast Genetic Testing Results in Neonatal Care using LUMINA

Leveraging EMR Data with AI to Forecast Genetic Testing Results in Neonatal Care using LUMINA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Leveraging EMR Data with AI to Forecast Genetic Testing Results in Neonatal Care using LUMINA Manan Vij, Jichao Chen, Daniel Swarr, Anne Slavotinek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7632718/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Accurate identification of patients likely to benefit from genetic testing in the neonatal intensive care unit (NICU) remains a clinical challenge. We introduce LUMINA (Learning from Unstructured Medical Information for Neonatal Assessment), a computational framework that leverages electronic medical record notes and Human Phenotype Ontology (HPO) embeddings to predict genetic testing outcomes. By integrating disease-specific databases, LUMINA captures phenotypic patterns that differentiate genetic from non-genetic patients. Evaluation of database-enriched HPO embeddings shows stronger disease-specific signals, while predictive models based on patient similarity matrices achieve robust discrimination and early identification of high-risk patients. Temporal analysis shows that LUMINA rapidly distinguishes genetic from non-genetic cases shortly after NICU admission, supporting timely decision-making. Importantly, the model’s predictions are interpretable: each patient’s classification is influenced by phenotypically similar patients, highlighting how shared clinical features drive the prediction. This approach provides a scalable, data-driven tool for neonatal precision medicine for future integration into clinical workflows. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Precision child health Rare diseases Genomic medicine Neonatal intensive care Human Phenotype Ontology (HPO) Predictive modeling Artificial Intelligence Deep Learning Full Text Additional Declarations No competing interests reported. Statement: All data were deidentified. This study was reviewed by the Cincinnati Children’s Hospital Institutional Review Board (IRB) and determined to be exempt from further review and oversight in accordance with the 2018 Requirements (IRB ID: 2024-0424; determination date: June 25, 2024) Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviews received at journal 03 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers invited by journal 26 Sep, 2025 Editor assigned by journal 25 Sep, 2025 Submission checks completed at journal 25 Sep, 2025 First submitted to journal 16 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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