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
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引用次数: 0

摘要

虽然药物摄入在孕妇中很常见,但对用药安全的研究仍然不足,导致对患者和医护人员的指导不明确。PregMedNet 基于对美国索赔数据库中 119 万个母婴二元组的系统分析,提供了一个多方面的孕产妇用药安全框架,从而弥补了这一不足。我们采用了一种新颖的混杂因素调整方法,系统地控制了多种药物-疾病配对的混杂因素,有力地识别了已知和新的孕产妇用药效应。值得注意的是,其中一项新发现的关联已通过实验验证,证明了索赔数据和机器学习在围产期用药安全研究中的可靠性。此外,新发现关联的潜在生物机制是通过图学习方法生成的。这些发现凸显了 PregMedNet 在促进孕期安全用药和孕产妇-新生儿结局方面的价值。
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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.

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