{"paper_id":"4a2dab9a-5400-4a67-bf36-963e4487cfcd","body_text":"A Causal Discovery Workflow for Rare Diseases: Experts-in-the-Loop Analysis of Sparse Longitudinal Data | 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 A Causal Discovery Workflow for Rare Diseases: Experts-in-the-Loop Analysis of Sparse Longitudinal Data Niccolò Rocchi, Alessio Zanga, Alice Bernasconi, Alessandro Gronchi, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6785120/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Causal networks provide a mechanistic understanding of clinical phenomena, allowing for personalized and explainable decision-making. Causal discovery, namely the task of constructing such models, is challenging, particularly for rare diseases, where observational data are sparse, medical knowledge is incomplete, and diseases behave over time. This work proposes a new and original expert-in-the-loop causal discovery workflow that iteratively refines a set of causal networks associated with different disease mechanisms.When applied to soft tissue sarcoma, a heterogeneous group of rare cancers, the workflow allows for the first comprehensive causal description of the disease's natural history. Indeed, three causal networks associated with different disease mechanisms shed light on the complex interplay between patients' covariates and disease behavior. These results have the potential to enhance clinical decision-making by allowing the development of personalized treatment strategies.The proposed workflow paves the way to agile, modular, and flexible causal discovery for clinical domains characterized by data sparsity, longitudinal dynamics, and heterogeneous expert knowledge. Causal discovery Prior knowledge Longitudinal data Rare diseases Soft tissue sarcoma Full Text Additional Declarations No competing interests reported. Supplementary Files 20240115SoftTissueSarcomaSTSCopySupplementaryMaterial.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Aug, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviews received at journal 29 Jul, 2025 Reviews received at journal 06 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers invited by journal 03 Jun, 2025 Editor assigned by journal 03 Jun, 2025 Submission checks completed at journal 02 Jun, 2025 First submitted to journal 30 May, 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. 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