Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning

Clinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. However, these variables result in low predictive accuracy. Here, we developed a prediction model for pregnancy-associated hypertension using graph-ba...

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Detalles Bibliográficos
Autores principales: Lee, Seung Mi, Nam, Yonghyun, Choi, Eun Saem, Jung, Young Mi, Sriram, Vivek, Leiby, Jacob S., Koo, Ja Nam, Oh, Ig Hwan, Kim, Byoung Jae, Kim, Sun Min, Kim, Sang Youn, Kim, Gyoung Min, Joo, Sae Kyung, Shin, Sue, Norwitz, Errol R., Park, Chan-Wook, Jun, Jong Kwan, Kim, Won, Kim, Dokyoon, Park, Joong Shin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499925/
https://www.ncbi.nlm.nih.gov/pubmed/36138035
http://dx.doi.org/10.1038/s41598-022-15391-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499925/
https://www.ncbi.nlm.nih.gov/pubmed/36138035
http://dx.doi.org/10.1038/s41598-022-15391-4

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