Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes

Author:

Khan Wasif,Zaki NazarORCID,Ahmad Amir,Masud Mohammad M.,Govender Romana,Rojas-Perilla Natalia,Ali Luqman,Ghenimi Nadirah,Ahmed Luai A.

Abstract

AbstractAdverse pregnancy outcomes, such as low birth weight (LBW) and preterm birth (PTB), can have serious consequences for both the mother and infant. Early prediction of such outcomes is important for their prevention. Previous studies using traditional machine learning (ML) models for predicting PTB and LBW have encountered two important limitations: extreme class imbalance in medical datasets and the inability to account for complex relational structures between entities. To address these limitations, we propose a node embedding-based graph outlier detection algorithm to predict adverse pregnancy outcomes. We developed a knowledge graph using a well-curated representative dataset of the Emirati population and two node embedding algorithms. The graph autoencoder (GAE) was trained by applying a combination of original risk factors and node embedding features. Samples that were difficult to reconstruct at the output of GAE were identified as outliers considered representing PTB and LBW samples. Our experiments using LBW, PTB, and very PTB datasets demonstrated that incorporating node embedding considerably improved performance, achieving a 12% higher AUC-ROC compared to traditional GAE. Our study demonstrates the effectiveness of node embedding and graph outlier detection in improving the prediction performance of adverse pregnancy outcomes in well-curated population datasets.

Funder

United Arab Emirates University

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference60 articles.

1. International Pregnancy | Guttmacher Institute. Accessed 24 May 2022. [Online]. Available: https://www.guttmacher.org/global/pregnancy

2. Bearak, J. et al. Unintended pregnancy and abortion by income, region, and the legal status of abortion: Estimates from a comprehensive model for 1990–2019. Lancet Glob. Health 8(9), e1152–e1161. https://doi.org/10.1016/S2214-109X(20)30315-6 (2020).

3. Number of births per year. Accessed 24 May 2022. [Online]. Available: https://www.theworldcounts.com/populations/world/births

4. Special Focus on Global Fertility WORLD POPULATION GLOBAL TOTAL FERTILITY RATE % OF ALL BIRTHS GLOBALLY TO MOTHERS AGES 35+.

5. Teitelman, A. M., Welch, L. S., Hellenbrand, K. G. & Bracken, M. B. Effect of maternal work activity on preterm birth and low birth weight. Am. J. Epidemiol. 131(1), 104–113. https://doi.org/10.1093/oxfordjournals.aje.a115463 (1990).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3