CHRONOS: Extracting Novel Spine Surgery Hypotheses from Historical Medical Texts

CHRONOS: Extracting Novel Spine Surgery Hypotheses from Historical Medical Texts | 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 CHRONOS: Extracting Novel Spine Surgery Hypotheses from Historical Medical Texts Nassim Dehouche, Léonard Chatelain, Virginie Lafage, Jean Meyblum, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6677562/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 This paper presents Chronos, a system for representing and revisiting overlooked historical and traditional medical insights using modern knowledge frameworks. Inspired by Nobel laureate Tu Youyou's systematic approach to mining traditional Chinese medicine for antimalarial compounds, Chronos employs large language models and decentralized knowledge graphs to formalize historical spine surgery observations into testable hypotheses. We implement the Hypothesis and Evidence (HypE) taxonomy to structure machine-readable, refutable hypotheses from historical and traditional medical texts, bridging centuries of medical knowledge. Using a comprehensive taxonomy spanning historical observations, formalized hypotheses, evidence elements, modern medical concepts, and research opportunities, we demonstrate Chronos' capabilities by generating novel research hypotheses from two sources: the works of Charles-Prosper Ollivier d'Angers (1824) and a 19th-century Thai traditional medicine compendium. These hypotheses—ranging from spinal venous congestion to gut-spine axis mechanisms—are evaluated for scientific merit and novelty, illustrating how AI-powered knowledge mining can uncover valuable insights from diverse historical medical literature. The Chronos system represents a scalable, automated approach to hypothesis generation with the potential to accelerate discovery across various medical domains. Artificial Intelligence and Machine Learning knowledge mining semantic networks hypothesis generation medical history artificial intelligence spine surgery 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-6677562","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457421842,"identity":"e2afaff0-a8dc-447e-ad85-e9cda409b7a4","order_by":0,"name":"Nassim 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