Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers

Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers | 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 Research Article Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers Shi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6150705/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Deep Learning has emerged as one of the most significant innovations in machine learning. However, a notable limitation of this field lies in the black box decision-making processes, which have led to skepticism within groups like healthcare and scientific communities regarding its applicability. In response, this study introduces a interpretable approach using Neural Ordinary Differential Equations (NODEs), a category of neural network models that exploit the dynamics of differential equations for representation learning. Leveraging their foundation in differential equations, we illustrate the capability of these models to continuously process textual data, marking the first such model of its kind, and thereby proposing a promising direction for future research in this domain. The primary objective of this research is to propose a novel architecture for groups like healthcare that require the predictive capabilities of deep learning while emphasizing the importance of model transparency demonstrated in NODEs. Theoretical Computer Science neural odes ai in health Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6150705","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":423713118,"identity":"298c9a30-9d06-4ede-a63f-3f35d3347057","order_by":0,"name":"Shi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCcbGAwkGNjxszIyND4B8Hj4itDQcSKhIk+Fjbz5sANLCRlgLA8MBhjOHbeR4jqVJgAQIapGf3dxw4GFbGg+bRI5Z5dccOxk2BuaHj27g0WJw52DDgcQ2G7CW27LbkoEOYzM2zsGnRSIRpAViy23JbcxALTxs0vi0yM8AazkM1lIsua2esBaGG0AtCWeAWoDeZ/y47TBhLQZgLRVAhwEDWZpx23FgBBHwi/yM9IcPfxjY2Ms3MzZ+/Lmt2p6fvfnhY7wOQwbMPGCSWOUgwPiDFNWjYBSMglEwYgAADzZJAb2Z05YAAAAASUVORK5CYII=","orcid":"","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Shi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-04 04:27:04","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6150705/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6150705/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78157066,"identity":"28dc192f-1394-4495-85f0-0f4357eb4da9","added_by":"auto","created_at":"2025-03-10 12:41:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":282123,"visible":true,"origin":"","legend":"","description":"","filename":"NeuralODEsasMedicalSolvers.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6150705/v1_covered_dca0a230-6d68-444a-8f18-a0a6e7cb293c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eExploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Columbia University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"neural odes, ai in health","lastPublishedDoi":"10.21203/rs.3.rs-6150705/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6150705/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDeep Learning has emerged as one of the most significant innovations in machine learning. 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