Analysis and Knowledge Extraction of Newborn Resuscitation Activities from Annotation Files

Analysis and Knowledge Extraction of Newborn Resuscitation Activities from Annotation Files | 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 Analysis and Knowledge Extraction of Newborn Resuscitation Activities from Annotation Files Mohanad Abukmeil, Øyvind Meinich-Bache, Trygve Eftestøl, Siren Rettedal, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4021411/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Nov, 2024 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted 15 You are reading this latest preprint version Abstract Deprivation of oxygen in a newbornduring and after birth leads to birth asphyxia, which is considered one of the leading causes of death in the neonatal period. Adequate resuscitation activities are performed immediately after birth to save the majority of newborns. The primary resuscitation activities include ventilation, stimulation, drying, suction, and chest compression. While resuscitation guidelines exist, little research has been conducted on measured resuscitation episodes. Modeling the executed resuscitation activities to generate temporal data and extract knowledge can provide unique insights into dominant resuscitation activities. It also aids in constructing a resuscitation timeline to visually represent and describe the actions performed on a newborn.In this paper, we propose a method for generating and encoding temporal resuscitation data, enabling the description and visualization of the resuscitation timeline. We utilize neighborhood component analysis (NCA) to cluster the generated data based on the presence of ventilation and the outcome of the newborn. Additionally, we employ an autoencoder (AE) model to enhance clustering performance by visualizing its latent space.Our proposed method demonstrates high-quality visual clustering results on two different datasets. It provides insights into the intricate structure of the generated resuscitation data by grouping similar unlabeled resuscitation episodes into coherent clusters. Newborn Resuscitation Activities Visualization Clustering Autoencoder Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Nov, 2024 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted Editorial decision: Revision requested 26 Aug, 2024 Reviews received at journal 07 Aug, 2024 Reviews received at journal 06 Aug, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers agreed at journal 20 Jul, 2024 Reviewers agreed at journal 20 Jul, 2024 Reviewers agreed at journal 19 Jul, 2024 Reviewers agreed at journal 19 Jul, 2024 Reviewers agreed at journal 18 Jul, 2024 Reviewers agreed at journal 22 May, 2024 Reviewers invited by journal 24 Apr, 2024 Editor invited by journal 07 Mar, 2024 Submission checks completed at journal 07 Mar, 2024 Editor assigned by journal 07 Mar, 2024 First submitted to journal 06 Mar, 2024 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-4021411","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277689041,"identity":"0a1c79db-802c-4ad3-85be-d7fc0d8ae4f9","order_by":0,"name":"Mohanad Abukmeil","email":"","orcid":"","institution":"University of Stavanger","correspondingAuthor":false,"prefix":"","firstName":"Mohanad","middleName":"","lastName":"Abukmeil","suffix":""},{"id":277689042,"identity":"c0eac100-147b-46a3-9b77-8e83eb39e1d9","order_by":1,"name":"Øyvind Meinich-Bache","email":"","orcid":"","institution":"University of 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