Research graph
References from Machine Learning–Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
External reference
Reducing disparities in severe maternal morbidity and mortality
10.1097/grf.0000000000000349 · External reference
A global view of severe maternal morbidity: moving beyond maternal mortality
10.1186/s12978-018-0527-2 · External reference
Frequency of and factors associated with severe maternal morbidity
10.1097/aog.0000000000000173 · External reference
Global causes of maternal death: a WHO systematic analysis
10.1016/s2214-109x(14)70227-x · External reference
Facility-based identification of women with severe maternal morbidity: it is time to start
10.1097/aog.0000000000000218 · External reference
Maternal near-miss case reviews: the UK approach
10.1111/1471-0528.12802 · External reference
Preventability of severe acute maternal morbidity
10.1016/j.ajog.2013.12.032 · External reference
Risk stratification and hierarchy of antenatal care
10.1016/j.bpobgyn.2014.03.004 · External reference
Early pregnancy screening for women at high-risk of GDM results in reduced neonatal morbidity and similar maternal outcomes to routine screening
10.1155/2020/9083264 · External reference
High-risk consult multidisciplinary team in a tertiary maternity unit: changing prevalence of cases
10.2147/jmdh.s502995 · External reference
Combining the perspectives of midwives and doctors improves risk assessment in early pregnancy
10.1080/00016340601104021 · External reference
The role of socioeconomic factors in Black-White disparities in preterm birth
10.2105/ajph.2014.302008 · External reference
Artificial intelligence, machine learning and health systems
10.7189/jogh.08.020303 · External reference
Applications of artificial intelligence in obstetrics
10.14366/usg.22063 · External reference
Cervical length screening for prevention of preterm birth in singleton pregnancy with threatened preterm labor: systematic review and meta-analysis of randomized controlled trials using individual patient-level data
10.1002/uog.17388 · External reference
Universal cervical length screening for preterm birth prevention in the United States
10.1080/14767058.2016.1220521 · External reference
Evaluating the use of the QUiPP app and its impact on the management of threatened preterm labour: a cluster randomised trial
10.1371/journal.pmed.1003689 · External reference
Use of artificial intelligence in obstetrics: not quite ready for prime time
10.1016/j.ajogmf.2022.100792 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Accurate predictions on small data with a tabular foundation model
10.1038/s41586-024-08328-6 · External reference
Unresolved reference
External reference
First-trimester prediction models based on maternal characteristics for adverse pregnancy outcomes: a systematic review and meta-analysis
10.1111/1471-0528.17983 · External reference
The assessment of combined first trimester screening in women of advanced maternal age in an Asian cohort
10.11622/smedj.2015009 · External reference
Comparison of diagnostic accuracy of early screening for pre-eclampsia by NICE guidelines and a method combining maternal factors and biomarkers: results of SPREE
10.1002/uog.19039 · External reference
Enriching real-world data with social determinants of health for health outcomes and health equity: successes, challenges, and opportunities
10.1055/s-0043-1768732 · External reference
Prediction model development of late-onset preeclampsia using machine learning-based methods
10.1371/journal.pone.0221202 · External reference
Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015
10.1038/s41598-020-62210-9 · External reference
Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study
10.1016/s2589-7500(23)00267-4 · External reference
Application of machine-learning to predict early spontaneous preterm birth among nulliparous non-Hispanic Black and White women
10.1016/j.annepidem.2018.08.008 · External reference
Prediction of obstetrical and fetal complications using automated electronic health record data
10.1016/j.ajog.2020.10.030 · External reference
Machine learning for social services: a study of prenatal case management in Illinois
10.2105/ajph.2017.303711 · External reference
