학술논문
Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study
Document Type
Article
Author
Montgomery-Csobán, Tünde; Kavanagh, Kimberley; Murray, Paul; Robertson, Chris; Barry, Sarah J E; Vivian Ukah, U; Payne, Beth A; Nicolaides, Kypros H; Syngelaki, Argyro; Ionescu, Olivia; Akolekar, Ranjit; Hutcheon, Jennifer A; Magee, Laura A; von Dadelszen, Peter; Brown, Mark A.; Davis, Gregory K.; Parker, Claire; Walters, Barry N.; Sass, Nelson; Ansermino, J. Mark; Cao, Vivien; Cundiff, Geoffrey W.; von Dadelszen, Emma C.M.; Douglas, M. Joanne; Dumont, Guy A.; Dunsmuir, Dustin T.; Hutcheon, Jennifer A.; Joseph, K.S.; Lalji, Sayrin; Lee, Tang; Li, Jing; Lim, Kenneth I.; Lisonkova, Sarka; Lott, Paula; Menzies, Jennifer M.; Millman, Alexandra L.; Palmer, Lynne; Payne, Beth A.; Qu, Ziguang; Russell, James A.; Sawchuck, Diane; Shaw, Dorothy; Still, D. Keith; Ukah, U. Vivian; Wagner, Brenda; Walley, Keith R.; Hugo, Dany; Gruslin, The late Andrée; Tawagi, George; Smith, Graeme N.; Côté, Anne-Marie; Moutquin, Jean-Marie; Ouellet, Annie B.; Lee, Shoo K.; Duan, Tao; Zhou, Jian; Haniff, The late Farizah; Mahajan, Swati; Noovao, Amanda; Karjalainend, Hanna; Kortelainen, Alja; Laivuori, Hannele; Ganzevoort, J. Wessel; Groen, Henk; Kyle, Phillipa M.; Moore, M. Peter; Pullar, Barbra; Bhutta, Zulfiqar A.; Qureshi, Rahat N.; Sikandar, Rozina; Bhutta, The late Shereen Z.; Cloete, Garth; Hall, David R.; van Papendorp, The late Erika; Steyn, D. Wilhelm; Biryabarema, Christine; Mirembe, Florence; Nakimuli, Annettee; Allotey, John; Thangaratinam, Shakila; Nicolaides, Kypros H.; Ionescu, Olivia; Syngelaki, Argyro; de Swiet, Michael; Magee, Laura A.; von Dadelszen, Peter; Akolekar, Ranjit; Walker, James J.; Robson, Stephen C.; Broughton-Pipkin, Fiona; Loughna, Pamela; Vatish, Manu; Redman, Christopher W.G.; Barry, Sarah J.E.; Kavanagh, Kimberley; Montgomery-Csobán, Tunde; Murray, Paul; Robertson, Chris; Tsigas, Eleni Z.; Woelkers, Douglas A.; Lindheimer, Marshall D.; Grobman, William A.; Sibai, Baha M.; Merialdi, Mario; Widmer, Mariana
Source
The Lancet Digital Health; April 2024, Vol. 6 Issue: 4 pe238-e250, 13p
Subject
Language
ISSN
25897500
Abstract
Affecting 2–4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia.