The Clinical Trial Duration Prediction Dataset: A Resource for AI Research

The Clinical Trial Duration Prediction Dataset: A Resource for AI Research | 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 data-descriptor The Clinical Trial Duration Prediction Dataset: A Resource for AI Research Yuta Nonomiya, Muhammad Nabeel Asim, Yume Takahashi, Koichi Kise, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8852939/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 Early forecasting of clinical trials duration supports planning, optimized resource allocation, and cost control. Developing effective AI predictors remains challenging, requiring collaboration between medical and computational expertise. Although a publicly available dataset exists for this task, it is not well-suited for development of an AI-driven predictor capable of accurately predicting clinical trials duration. This limitation arises from its lack of critical features that significantly influence trials length, as well as the presence of incompletely and inappropriately selected trials. To empower development of AI predictors, we introduce the Clinical Trial Duration Prediction (CTDP) dataset, derived from 81,943 completed drug trials across 1,520 ICD-10 categories. The CTDP dataset contains 51 features on clinical trial characteristics including phase, purpose, condition, eligibility, enrollment, arm information, design, endpoint, site, sponsor, and summary that together influence trial duration. CTDP dataset development through collaboration between clinical trial experts and AI researchers will enable the creation of robust and accurate AI-driven applications for predicting clinical trial durations. Clinical Trial Clinical Trial Duration Prediction Machine Learning Artificial Intelligence Clinical Trial Dataset AI-supported Clinical Trial Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterial1.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. 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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-8852939","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":596119063,"identity":"6e411b28-7dc0-442a-94cf-facb072466cd","order_by":0,"name":"Yuta 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