Yeasul Kim, Ivana Marić, Chloe M Kashiwagi, Lichy Han, Philip Chung, Jonathan D Reiss, Lindsay D Butcher, Kaitlin J Caoili, Eloïse Berson, Lei Xue, Camilo Espinosa, Tomin James, Sayane Shome, Feng Xie, Marc Ghanem, David Seong, Alan L Chang, S Momsen Reincke, Samson Mataraso, Chi-Hung Shu, Davide De Francesco, Martin Becker, Wasan M Kumar, Ron Wong, Brice Gaudilliere, Martin S Angst, Gary M Shaw, Brian T Bateman, David K Stevenson, Lance S Prince, Nima Aghaeepour
{"title":"PregMedNet: Multifaceted Maternal Medication Impacts on Neonatal Complications.","authors":"Yeasul Kim, Ivana Marić, Chloe M Kashiwagi, Lichy Han, Philip Chung, Jonathan D Reiss, Lindsay D Butcher, Kaitlin J Caoili, Eloïse Berson, Lei Xue, Camilo Espinosa, Tomin James, Sayane Shome, Feng Xie, Marc Ghanem, David Seong, Alan L Chang, S Momsen Reincke, Samson Mataraso, Chi-Hung Shu, Davide De Francesco, Martin Becker, Wasan M Kumar, Ron Wong, Brice Gaudilliere, Martin S Angst, Gary M Shaw, Brian T Bateman, David K Stevenson, Lance S Prince, Nima Aghaeepour","doi":"10.1101/2025.02.13.25322242","DOIUrl":null,"url":null,"abstract":"<p><p>While medication intake is common among pregnant women, medication safety remains underexplored, leading to unclear guidance for patients and healthcare professionals. PregMedNet addresses this gap by providing a multifaceted maternal medication safety framework based on systematic analysis of 1.19 million mother-baby dyads from U.S. claims databases. A novel confounding adjustment pipeline was applied to systematically control confounders for multiple medication-disease pairs, robustly identifying both known and novel maternal medication effects. Notably, one of the newly discovered associations was experimentally validated, demonstrating the reliability of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms of newly identified associations were generated using a graph learning method. These findings highlight PregMedNet's value in promoting safer medication use during pregnancy and maternal-neonatal outcomes.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844599/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv : the preprint server for health sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2025.02.13.25322242","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PregMedNet: Multifaceted Maternal Medication Impacts on Neonatal Complications.
While medication intake is common among pregnant women, medication safety remains underexplored, leading to unclear guidance for patients and healthcare professionals. PregMedNet addresses this gap by providing a multifaceted maternal medication safety framework based on systematic analysis of 1.19 million mother-baby dyads from U.S. claims databases. A novel confounding adjustment pipeline was applied to systematically control confounders for multiple medication-disease pairs, robustly identifying both known and novel maternal medication effects. Notably, one of the newly discovered associations was experimentally validated, demonstrating the reliability of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms of newly identified associations were generated using a graph learning method. These findings highlight PregMedNet's value in promoting safer medication use during pregnancy and maternal-neonatal outcomes.