Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a singl...

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Bibliographic Details
Main Authors: Malacova, Eva, Tippaya, Sawitchaya, Bailey, Helen, Chai, Kevin, Farrant, B.M., Gebremedhin, Amanuel, Leonard, H., Marinovich, Luke, Nassar, N., Phatak, Aloke, Raynes-Greenow, C., Regan, Annette, Shand, A.W., Shepherd, Carrington, Srinivasjois, Ravisha, Tessema, Gizachew, Pereira, Gavin
Format: Journal Article
Language:English
Published: NATURE PORTFOLIO 2020
Subjects:
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Algorithms
Cohort Studies
Female
Humans
Live Birth
Machine Learning
Maternal Age
Pregnancy
Pregnancy Complications
Prenatal Care
Reproductive History
Risk Assessment
Socioeconomic Factors
Stillbirth
Western Australia
Online Access:http://purl.org/au-research/grants/arc/IC180100030
http://hdl.handle.net/20.500.11937/90949

Internet

http://purl.org/au-research/grants/arc/IC180100030
http://hdl.handle.net/20.500.11937/90949

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