Research on Automatic Classification and Prognosis Prediction of Intracerebral Hemorrhage Based on Deep Learning Models

Research on Automatic Classification and Prognosis Prediction of Intracerebral Hemorrhage Based on Deep Learning Models | 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 Research on Automatic Classification and Prognosis Prediction of Intracerebral Hemorrhage Based on Deep Learning Models Ying Mao, Xiaoyu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7169740/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 Intracerebral hemorrhage (ICH) poses significant challenges in clinical management due to its high morbidity and mortalityrates. Traditional prognostic models often rely on manual assessment and conventional statistical methods, which maylack the precision and adaptability required for individualized patient care. These approaches are limited in their ability tointegrate complex, high-dimensional data, leading to suboptimal predictive performance. To address these shortcomings, wepropose a novel deep learning framework that leverages advanced computational techniques to enhance the accuracy of ICHclassification and outcome prediction. Our methodology encompasses three core components: a symbolic representation ofanatomical regions to capture the spatial dynamics of hemorrhage propagation; structured neural modules embedded withgeometric priors derived from neuroanatomy to model the interplay between lesion topography and functional impact; anda dynamic factorization technique that decouples observed clinical scores from underlying vascular dynamics, thereby disentangling confounding vascular events from observable patient trajectories. This integrative approach enables fine-grainedtemporal reasoning and facilitates the incorporation of heterogeneous data modalities, including imaging, vital signs, andclinical notes. Experimental results model outperforms existing prognostic tools, providing more accurate and interpretablepredictions of patient outcomes. By embedding domain-specific constraints and causality-inspired latent-variable formulations,our framework offers a robust and scalable solution for personalized ICH management, aligning with the interdisciplinaryfocus of computational sciences in advancing healthcare technologies. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Deep Learning Intracerebral Hemorrhage Prognosis Prediction Neuroimaging Analysis Computational Modeling Full Text Additional Declarations No competing interests reported. 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-7169740","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538595010,"identity":"da5eb6fb-ad54-437f-a56c-053a39df6bf4","order_by":0,"name":"Ying Mao","email":"","orcid":"","institution":"Department of Critical Care Medicine, Affiliated Brain Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Mao","suffix":""},{"id":538595013,"identity":"158892ce-62be-4185-b9ef-1ee6cd9bc94f","order_by":1,"name":"Xiaoyu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3Pv0rDQBzA8ZNAu1zIJlcs5BUukw4hfZU7Ds4lSkAoHdMlDga6JvgSlUDnhAOn2K5KlkydHDJmKuZq6yDpdRW873I3/D73BwCd7o/WkAGRKxYtdPc7QAKluEh+CBhzAgaS4DMEHIgBXPFNgIJcP8ZOWE+5txiKFxH4m3vLfqJNjYFtXea9ZFyWTkjWPktjHoikrB5GkZmh7mFO+kx6CUL8tqbRjOHcx7kZV3T5ai4lIbg6QewtDfdk89mR3VqSrFUSZBQd8T387mMBYS7JSn0LZEX3F05GyTYoEshoGt2tbghGp/8yLObzdsomlsWypoEeXRhv2Uc7c23rqp8co+Hvs5TjssnZCZ1Op/u/fQFvYmdTcyfh9QAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Equipment, Affiliated Brain Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-20 12:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7169740/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7169740/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94984435,"identity":"7d4d7b57-3ddc-4ebb-936c-276c63905715","added_by":"auto","created_at":"2025-11-03 06:34:02","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4699,"visible":true,"origin":"","legend":"","description":"","filename":"6bfe83f0ca7048049bcc942529380a1f.json","url":"https://assets-eu.researchsquare.com/files/rs-7169740/v1/42d2e62a28043a19b5594209.json"},{"id":101831137,"identity":"cd3b0fda-4dd2-4aec-b968-018b42126d99","added_by":"auto","created_at":"2026-02-04 06:27:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2678413,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7169740/v1_covered_09c2ed31-aa7e-47fc-b4f8-8436201c55cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eResearch on Automatic Classification and Prognosis Prediction of Intracerebral Hemorrhage Based on Deep Learning Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Deep Learning, Intracerebral Hemorrhage, Prognosis Prediction, Neuroimaging Analysis, Computational Modeling","lastPublishedDoi":"10.21203/rs.3.rs-7169740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7169740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Intracerebral hemorrhage (ICH) poses significant challenges in clinical management due to its high morbidity and mortalityrates. Traditional prognostic models often rely on manual assessment and conventional statistical methods, which maylack the precision and adaptability required for individualized patient care. These approaches are limited in their ability tointegrate complex, high-dimensional data, leading to suboptimal predictive performance. To address these shortcomings, wepropose a novel deep learning framework that leverages advanced computational techniques to enhance the accuracy of ICHclassification and outcome prediction. Our methodology encompasses three core components: a symbolic representation ofanatomical regions to capture the spatial dynamics of hemorrhage propagation; structured neural modules embedded withgeometric priors derived from neuroanatomy to model the interplay between lesion topography and functional impact; anda dynamic factorization technique that decouples observed clinical scores from underlying vascular dynamics, thereby disentangling confounding vascular events from observable patient trajectories. This integrative approach enables fine-grainedtemporal reasoning and facilitates the incorporation of heterogeneous data modalities, including imaging, vital signs, andclinical notes. Experimental results model outperforms existing prognostic tools, providing more accurate and interpretablepredictions of patient outcomes. 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