Vitabel: Bridging Clinical Expertise and the Machine Learning Pipeline in Critical Care

Vitabel: Bridging Clinical Expertise and the Machine Learning Pipeline in Critical Care | 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 Vitabel: Bridging Clinical Expertise and the Machine Learning Pipeline in Critical Care Simon Orlob, Wolfgang J. Kern, Benjamin Hackl, Jan Wnent, Jan-Thorsten Gräsner, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7227195/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Artificial intelligence offers a great opportunity in critical care, particularly when a vast amount of continuously acquired physiological data is incorporated. High-quality, reliably labelled data are paramount for developing and training artificial intelligence methods. However, routinely recorded data in critical care are often noisy, and the sheer volume of high-resolution data is challenging to manage. Generalizable solutions for these problems are lacking, restricting progress.To address these barriers, we developed \vitabel{}, an open-source \python{} package for loading, visualising, aligning, and annotating medical time series with minimal coding. The tool integrates seamlessly into \jupyter{-notebooks}, providing an interactive, customizable interface to interact with the data visually. In this publication, we demonstrate its utility across three use cases. The code and exemplary data are provided as browser-based demos. \vitabel{} is freely available and published under the MIT license accompanying this publication. critical care resuscitation python software labelling time series data Full Text Additional Declarations Competing interest reported. The authors declare no competing interests regarding the manuscript’s content. MH and WK acknowledge funding from the University of Graz within the project “A machine learning approach towards data-driven cardiopulmonary resuscitation”. The current position of SO is sponsored by the DAMP Foundation within the Cardiac Arrest Research Fellowship program. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Feb, 2026 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 26 Oct, 2025 Editor assigned by journal 10 Aug, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 27 Jul, 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. 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-7227195","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519285915,"identity":"6c377709-aba9-4035-bfe9-c08414053134","order_by":0,"name":"Simon Orlob","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACZijNx8DD+IDhAEMCAwgRpYWNgYfZgDgtMADUwiZBlBb5du7ExwUMNvls7L3HqnnO1OUxsCcfwKvF4DDvZuMZDGmWbTzn0m7z3DhczMDzDL81Bsy826R5GA4bsEnkmN3m+XAgsUEixwC/w5qRtBTzfKgDasn/gN8zh5G0MPPcYAbZgl8H2C88BmkGbDznkiXnnDmc2MbzjIDD+s9ufMxTYWPAz9578MObY3WJ/ezJD/BbA7ELic1GhPpRMApGwSgYBQQAAOD7PmSWvo+PAAAAAElFTkSuQmCC","orcid":"","institution":"University Hospital Schleswig-Holstein","correspondingAuthor":true,"prefix":"","firstName":"Simon","middleName":"","lastName":"Orlob","suffix":""},{"id":519285916,"identity":"ac9e6b64-9026-4287-bbad-c1452283c24d","order_by":1,"name":"Wolfgang J. 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The authors declare no competing interests regarding the manuscript’s content.\nMH and WK acknowledge funding from the University of Graz within the project “A machine learning approach towards data-driven cardiopulmonary resuscitation”.\nThe current position of SO is sponsored by the DAMP Foundation within the Cardiac Arrest Research Fellowship program.","formattedTitle":"Vitabel: Bridging Clinical Expertise and the Machine Learning Pipeline in Critical Care","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-medical-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Medical Systems](https://www.springer.com/journal/10916)","snPcode":"10916","submissionUrl":"https://submission.nature.com/new-submission/10916/3","title":"Journal of Medical Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"critical care, resuscitation, python, software, labelling, time series data","lastPublishedDoi":"10.21203/rs.3.rs-7227195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7227195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence offers a great opportunity in critical care, particularly when a vast amount of continuously acquired physiological data is incorporated. 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