Deep Magnitude Management of Clinical Code Embeddings to Predict Unplanned Hospital Readmissions

Deep Magnitude Management of Clinical Code Embeddings to Predict Unplanned Hospital Readmissions | 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 Deep Magnitude Management of Clinical Code Embeddings to Predict Unplanned Hospital Readmissions Nzamba Bignoumba, Markus Bertl, Nedra Mellouli, Sadok Ben Yahia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4705729/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 The rate of unplanned hospital readmissions is a relevant indicator of the quality of care provided. From a financial point of view, unplanned readmissions are costly for patients and healthcare providers. Awareness of unplanned readmissions helps to mitigate the growth of healthcare costs. Several predictive models have been proposed to reduce unplanned hospital readmissions. However, most research on the topic does not consider the time elapsed between visits, when historical medical events are included in said research. Additionally, the focus on frequent medical events such as chronic illnesses that may lead to unplanned readmission is not explicitly captured in these models. The failure to consider elapsed time between visits and frequent or chronic illnesses can lead to a decline in the model's performance. We then introduce a model called ' Deep Magnitude Management ' (D2M) that incorporates the above-mentioned aspects. D2M handles sequences of visits according to their corresponding date and incorporates an explicit transfer mechanism that enables it to focus on frequent medical events. To provide effective evidence, we compare the performance of D2M with the state-of-the-art models and conduct various ablation studies using the MIMIC-3 database. Furthermore, we provide a series of charts explaining the results of the predictions. Medical Informatics Artificial Intelligence and Machine Learning Artificial Intelligence Deep Learning Decision Explainability Healthcare Hospital Readmission Prediction Support System. 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. 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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-4705729","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324181206,"identity":"6b6411ed-cfd4-4383-990c-69310173081e","order_by":0,"name":"Nzamba Bignoumba","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-2539-8831","institution":"Tallinn University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Nzamba","middleName":"","lastName":"Bignoumba","suffix":""},{"id":324181554,"identity":"986978cd-2ff6-4ff0-875b-583fddb19e35","order_by":1,"name":"Markus Bertl","email":"","orcid":"","institution":"Tallinn University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Bertl","suffix":""},{"id":324182794,"identity":"044f51aa-f553-4a9e-8303-53fe7cb905de","order_by":2,"name":"Nedra Mellouli","email":"","orcid":"","institution":"Léonard de Vinci Group","correspondingAuthor":false,"prefix":"","firstName":"Nedra","middleName":"","lastName":"Mellouli","suffix":""},{"id":324182795,"identity":"49e593fb-5ea5-4641-923a-70efdf72dd32","order_by":3,"name":"Sadok Ben Yahia","email":"","orcid":"","institution":"Tallinn University of Technology, University of Southern Denmark","correspondingAuthor":false,"prefix":"","firstName":"Sadok","middleName":"Ben","lastName":"Yahia","suffix":""}],"badges":[],"createdAt":"2024-07-08 12:59:15","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-4705729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4705729/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59964000,"identity":"ea63d4aa-2cd2-415f-b871-d6d9d112e0b8","added_by":"auto","created_at":"2024-07-10 01:41:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1340811,"visible":true,"origin":"","legend":"","description":"","filename":"D2M.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4705729/v1_covered_425d3ec0-eaf7-4dfd-b930-d5ea61eb2188.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDeep Magnitude Management of Clinical Code Embeddings to Predict Unplanned Hospital Readmissions\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tallinn University of Technology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Artificial Intelligence, Deep Learning, Decision Explainability, Healthcare, Hospital Readmission Prediction, Support System.","lastPublishedDoi":"10.21203/rs.3.rs-4705729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4705729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rate of unplanned hospital readmissions is a relevant indicator of the quality of care provided. 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