An imbalance-aware deep neural network for early prediction of preeclampsia

Preeclampsia (PE) is a hypertensive complication affecting 8-10% of US pregnancies annually. While there is no cure for PE, aspirin may reduce complications for those at high risk for PE. Furthermore, PE disproportionately affects racial minorities, with a higher burden of morbidity and mortality. P...

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Veröffentlicht in:	PloS one 2022-04, Vol.17 (4), p.e0266042-e0266042
Hauptverfasser:	Bennett, Rachel, Mulla, Zuber D, Parikh, Pavan, Hauspurg, Alisse, Razzaghi, Talayeh
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Analysis Artificial neural networks Aspirin Bayes Theorem Bayesian analysis Biology and Life Sciences Blood pressure Care and treatment Complications Computer and Information Sciences Computer architecture Datasets Demographic variables Diagnosis Entropy Entropy (Information theory) Feature selection Female Gynecology Health aspects Health sciences Hospitals Humans Infant Mathematical models Medicine Minority & ethnic groups Modelling Morbidity Neural networks Neural Networks, Computer Obstetrics Optimization Patients People and places Populations Pre-eclampsia Pre-Eclampsia - diagnosis Pre-Eclampsia - prevention & control Predictions Preeclampsia Pregnancy Pregnant women Race factors Sparsity Systems engineering Variables Womens health
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