Výsledky vyhledávání - "Hailey K Ballard"

Akademický článek

Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study

Autor: Hailey K Ballard, Xiaotong Yang, Aditya D Mahadevan, Dominick J Lemas, Lana X Garmire

Publikováno v: Journal of Medical Internet Research, Vol 26, p e48997 (2024)

Background Preeclampsia is a potentially fatal complication during pregnancy, characterized by high blood pressure and the presence of excessive proteins in the urine. Due to its complexity, the prediction of preeclampsia onset is often difficult and

Externí odkaz: https://doaj.org/article/65fe4096672e42988df8bd1351cf3fb9

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Building and validating 5-feature models to predict preeclampsia onset time from electronic health record data

Autor: Hailey K Ballard, Xiaotong Yang, Aditya Mahadevan, Dominick J Lemas, Lana X Garmire

BackgroundPreeclampsia is a potentially fatal complication during pregnancy, characterized by high blood pressure and presence of proteins in the urine. Due to its complexity, prediction of preeclampsia onset is often difficult and inaccurate.Methods

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::515c18af9121363320a2f24bc78e057b
https://doi.org/10.1101/2023.03.23.23287655

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Deep learning-based prognosis models accurately predict the time to delivery among preeclamptic pregnancies using electronic health record

Autor: Xiaotong Yang, Hailey K Ballard, Aditya D Mahadevan, Ke Xu, David G Garmire, Elizabeth S Langen, Dominick J Lemas, Lana X Garmire

Structured AbstractBackgroundPreeclampsia (PE) is one of the leading factors in maternal and perinatal mortality and morbidity worldwide. Delivery timing is key to balancing the risk between severe maternal and neonatal morbidities in pregnancies com

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::477b96ac293f0c9c7ce7353893829663
https://doi.org/10.1101/2022.04.03.22273366

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