| Autor: |
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 |
| Zdroj: |
The Lancet Digital Health; April 2024, Vol. 6 Issue: 4 pe238-e250, 13p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
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