RUBIN: A Flexible, Bayesian Network-Based Clinical Decision Support System

RUBIN: A Flexible, Bayesian Network-Based Clinical Decision Support System | 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 RUBIN: A Flexible, Bayesian Network-Based Clinical Decision Support System Anna Kleinau, Marike Lombaers, Anna Schoenaker, Johanna MA Pijnenborg, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9404529/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 Background: Bayesian Networks (BNs) are promising models for clinical decision support. Nevertheless, they have rarely been adopted in clinical practice since only a few practitioners have the expertise to work with them. Carefully designed human-computer interfaces can guide and empower clinicians to use BNs to their full potential. However, most BN-based clinical decision support systems (CDSS) and their interfaces are tailored to a specific use case, hindering reusability. CDSS that support diverse BNs often lack the adaptability to tailor their interfaces to specific decision support scenarios, which is crucial for adoption in clinical practice. Methods: We developed RUBIN, a CDSS that supports diverse BNs while still providing a highly adaptable interface for specific decision support tasks. We achieve this by separating the core CDSS from the necessary adaptations using external customization files. RUBIN is designed with a strong emphasis on usability and reusability, tailored to the specific demands of clinical environments. It enables clinicians to work effectively with BNs without requiring technical expertise. Results: We iteratively evaluated RUBIN for preoperative risk stratification in endometrial cancer therapy and designed customizations to the CDSS for this task in close collaboration with gynecological oncologists. The final evaluation demonstrated the system's high usability for healthcare practitioners. To illustrate that these benefits extend beyond a single domain, we conducted a second case study on cardiovascular disease prediction, highlighting the system’s generalizability. Conclusions: By separating core CDSS functionality from domain-specific adaptations, RUBIN supports BN-based decision support without requiring specific BN knowledge and remains highly adaptable to new medical tasks. Human Computer Interaction health informatics Bayesian networks CDSS Full Text Additional Declarations No competing interests reported. All participants provided informed consent to participate in this study. Supplementary Files Userinterfacetestv4.pdf 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-9404529","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623473146,"identity":"e65a82e8-33cd-4db7-b2db-3732698f8b06","order_by":0,"name":"Anna 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Nevertheless, they have rarely been adopted in clinical practice since only a few practitioners have the expertise to work with them. Carefully designed human-computer interfaces can guide and empower clinicians to use BNs to their full potential. However, most BN-based clinical decision support systems (CDSS) and their interfaces are tailored to a specific use case, hindering reusability. CDSS that support diverse BNs often lack the adaptability to tailor their interfaces to specific decision support scenarios, which is crucial for adoption in clinical practice. \nMethods: We developed RUBIN, a CDSS that supports diverse BNs while still providing a highly adaptable interface for specific decision support tasks. We achieve this by separating the core CDSS from the necessary adaptations using external customization files. RUBIN is designed with a strong emphasis on usability and reusability, tailored to the specific demands of clinical environments. 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