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  5. Prediction of Large for Gestational Age Infants in Overweight and Obese Women at Approximately 20 Gestational Weeks
 
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Prediction of Large for Gestational Age Infants in Overweight and Obese Women at Approximately 20 Gestational Weeks

Author(s)
Du, Yuhan  
Mehegan, John  
McAuliffe, Fionnuala M.  
Mooney, Catherine  
Uri
http://hdl.handle.net/10197/12183
Date Issued
2020-09-24
Date Available
2021-05-20T08:40:13Z
Abstract
Large for gestational age (LGA) births are associated with many maternal and perinatal complications. As overweight and obesity are risk factors for LGA, we aimed to predict LGA in overweight and obese women at approximately 20 gestational weeks, so that we can identify women at risk of LGA early to allow for appropriate interventions. A random forest algorithm was applied to maternal characteristics and blood biomarkers at baseline and 20 gestational weeks' ultrasound scan findings to develop a prediction model. Here we present our preliminary results demonstrating potential for use in clinical decision support for identifying patients early in pregnancy at risk of an LGA birth.
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2020 the Authors
Subjects

Health informatics

Infant birth weight

Prediction models

DOI
10.1145/3388440.3414906
Web versions
http://acm-bcb.org/2020/index.php
Language
English
Status of Item
Peer reviewed
Journal
BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
Conference Details
The 11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), Virtual Conference, 21-24 September 2020
ISBN
9781450379649
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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SIGBio_poster_2020_Yuhan.pdf

Size

293.54 KB

Format

Adobe PDF

Checksum (MD5)

4758bd59d39d0dbee4e12441100a3b84

Owning collection
Computer Science Research Collection
Mapped collections
Institute of Food and Health Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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