Suffering in Silence. Population-Level Detection and Risk-Prediction of Women’s Reproductive Disease from Claims Data

In: Research Square · 2023 · doi:10.21203/rs.3.rs-2670098/v1 · W4327733928
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This study developed data-driven predictive models using medical claims data to enable early detection and risk prediction of women's reproductive diseases like endometriosis, PCOS, and infertility up to two years in advance.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 · read from full text ⓘ

The preprint presents data-driven predictive models to detect and forecast risk for stigmatized, underserved women’s reproductive conditions using population-scale medical claims data. The approach aims to identify early and late stage risk factors up to two years before onset for endometriosis, polycystic ovarian syndrome, and infertility, enabling early detection at low cost. The paper is explicitly positioned as a preprint and notes it has not been peer reviewed by a journal. This paper is centrally about endometriosis — it develops claims-data risk prediction models that target endometriosis for earlier detection.

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Abstract

Abstract We present a new approach to population health where data-driven predictive models are learned for various stigmatized, underserved, and neglected chronic conditions that affect women. Our approach enables early detection within large populations at low cost from readily available medical claims datasets. The model uncovers early and late stage risk factors up to two years in advance of onset that can be used as guides for early interventions for endometriosis, polycystic ovarian syndrome, and infertility, allowing women suffering from these conditions to get on treatment plans earlier.
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Suffering in Silence. Population-Level Detection and Risk-Prediction of Women’s Reproductive Disease from Claims 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 Article Suffering in Silence. Population-Level Detection and Risk-Prediction of Women’s Reproductive Disease from Claims Data Maja Rudinac, Dimitrios Moirogiannis, Katie Baker, Thodoris Makridakis, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2670098/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract We present a new approach to population health where data-driven predictive models are learned for various stigmatized, underserved, and neglected chronic conditions that affect women. Our approach enables early detection within large populations at low cost from readily available medical claims datasets. The model uncovers early and late stage risk factors up to two years in advance of onset that can be used as guides for early interventions for endometriosis, polycystic ovarian syndrome, and infertility, allowing women suffering from these conditions to get on treatment plans earlier. Full Text Additional Declarations There is NO Competing Interest. Supplementary Files PaperSupplementaryMaterial.zip Cite Share Download PDF Status: Under Review 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. 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