{"paper_id":"312c241f-157a-440f-bffe-e09e976848cb","body_text":"FIR-LSTM: An Explainable Deep Learning Framework for Predicting Iatrogenic Withdrawal Syndrome in Pediatric Intensive Care Units | 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 FIR-LSTM: An Explainable Deep Learning Framework for Predicting Iatrogenic Withdrawal Syndrome in Pediatric Intensive Care Units Liqing Zhang, Haoqiu Song, Anita Patel, Murray Pollack, Layne Watson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6787167/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 Iatrogenic withdrawal syndrome (IWS) is a significant yet underrecognized public health concern for pediatric patients in critical care units, most frequently the result of abrupt cessation or rapid tapering of sedative or opioid medications. Early prediction of IWS is important for timely intervention and improved patient outcomes. In this study, we developed an explainable deep learning model utilizing a unidirectional multilayer long short-term memory (LSTM) network to predict the risk of IWS in pediatric ICU patients. Through longitudinal electronic health records (EHRs), our model analyzes the preceding 24 hours of patient data to predict the likelihood of IWS occurring in the next four hours, providing a real-time risk score. To enhance interpretability and identify key risk factors, we applied layer-wise relevance propagation (LRP) to the LSTM model. The feature importance rankings derived from LRP were validated through multiple experiments. Experimental results show that the model was perfectly calibrated and achieved robust predictive performance, suggesting that the LRP enhanced LSTM model holds significant potential for improving pediatric patient care by facilitating early detection and proactive management of IWS in critical care settings. Implementing this model into a system of alerts for clinicians could lead to significant advances in safer sedative and analgesic use, addressing an under addressed public health issue that impacts not only the United States but also the global community. Health sciences/Medical research/Paediatric research Health sciences/Health care/Paediatrics/Paediatric research Iatrogenic Withdrawal Syndrome Explainable AI Electronic Health Records Critical Care Multivariate Time Series Prediction Full Text Additional Declarations There is NO Competing Interest. Supplementary Files AppendixA.docx The Withdrawal Assessment Tool-1 (WAT-1) AppendixB.docx Feature Descriptions 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. 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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