WLENet: An Workload-Aware Physician Recommendation System with Patient Collective Learning

WLENet: An Workload-Aware Physician Recommendation System with Patient Collective Learning | 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 WLENet: An Workload-Aware Physician Recommendation System with Patient Collective Learning Siyuan Xu, Siyuan Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7238235/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract As a powerful online healthcare service tool, physician recommendation systems enable patients to swiftly and precisely locate suitable physicians at a professional level. However, most existing recommendation systems rarely account for physicians’ workloads, leading to potential inefficiencies in medical resource allocation. To address this practical challenge and research gap, this study proposes a novel deep learning model based on Gated Recurrent Units that explicitly incorporates physicians’ workloads constraints. Specifically, the physician’s workload is calculated by the case complexity of the current consultation and the physician’s remaining consultation capacity. To adapt the GRU framework for this task, a load gate and a time gate are added into to the GRU model, using physician’s workload to control the model’s attention to historical consultation records or the current consultation. Meanwhile, to address the issue of insufficient patient feature information, we develop a patient information enhancement module by extracting collective features from clustered similar patients. The model’s performance is rigorously evaluated using real-world data from Haodf.com, a leading Chinese online health community. Experimental results demonstrate that our model achieves state-of-the-art performance, validating its effectiveness in balancing workload-aware recommendations with accuracy. Recommendation system Physicians’ workloads Online health community Deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Nov, 2025 Reviews received at journal 21 Oct, 2025 Reviews received at journal 20 Oct, 2025 Reviews received at journal 18 Oct, 2025 Reviews received at journal 17 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviews received at journal 03 Oct, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviewers invited by journal 30 Sep, 2025 Editor assigned by journal 30 Sep, 2025 Submission checks completed at journal 03 Aug, 2025 First submitted to journal 28 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-7238235","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527894928,"identity":"d148bb3f-ef5c-466e-b969-09d04b57a06a","order_by":0,"name":"Siyuan Xu","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Siyuan","middleName":"","lastName":"Xu","suffix":""},{"id":527894930,"identity":"5f495b4a-4d33-4bc8-8198-4318a37da824","order_by":1,"name":"Siyuan 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Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer 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However, most existing recommendation systems rarely account for physicians\u0026rsquo; workloads, leading to potential inefficiencies in medical resource allocation. To address this practical challenge and research gap, this study proposes a novel deep learning model based on Gated Recurrent Units that explicitly incorporates physicians\u0026rsquo; workloads constraints. Specifically, the physician\u0026rsquo;s workload is calculated by the case complexity of the current consultation and the physician\u0026rsquo;s remaining consultation capacity. To adapt the GRU framework for this task, a load gate and a time gate are added into to the GRU model, using physician\u0026rsquo;s workload to control the model\u0026rsquo;s attention to historical consultation records or the current consultation. Meanwhile, to address the issue of insufficient patient feature information, we develop a patient information enhancement module by extracting collective features from clustered similar patients. The model\u0026rsquo;s performance is rigorously evaluated using real-world data from Haodf.com, a leading Chinese online health community. Experimental results demonstrate that our model achieves state-of-the-art performance, validating its effectiveness in balancing workload-aware recommendations with accuracy.\u003c/p\u003e","manuscriptTitle":"WLENet: An Workload-Aware Physician Recommendation System with Patient Collective Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 06:42:51","doi":"10.21203/rs.3.rs-7238235/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-01T21:42:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T18:03:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T12:14:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T23:14:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T07:34:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287577290523970048341313160414775596727","date":"2025-10-06T07:36:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186663036708588840006669425162783042874","date":"2025-10-04T00:32:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14942886780830693802124602491930191704","date":"2025-10-03T16:10:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94188824549557169715935210479157720798","date":"2025-10-03T14:31:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T14:12:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44957332619512631801046718002806328541","date":"2025-10-03T03:04:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162842110605742172132813996376811221051","date":"2025-10-03T01:25:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-01T00:45:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-01T00:31:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-03T15:06:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Supercomputing","date":"2025-07-29T02:37:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b7bf269f-7359-4e0b-b6d9-c9ae15d206f4","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-16T19:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 06:42:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7238235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7238235","identity":"rs-7238235","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}