Predicting risk of stillbirth and preterm pregnancies with machine learning
Modelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7096343/ https://www.ncbi.nlm.nih.gov/pubmed/32226625 http://dx.doi.org/10.1007/s13755-020-00105-9 |
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author | Koivu, Aki Sairanen, Mikko |
author_facet | Koivu, Aki Sairanen, Mikko |
author_sort | Koivu, Aki |
collection | PubMed |
description | Modelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study, we utilize state-of-the-art machine learning methods in the task of predicting early stillbirth, late stillbirth and preterm birth pregnancies. The aim of this experimentation is to discover novel risk models that could be utilized in a clinical setting. A CDC data set of almost sixteen million observations was used conduct feature selection, parameter optimization and verification of proposed models. An additional NYC data set was used for external validation. Algorithms such as logistic regression, artificial neural network and gradient boosting decision tree were used to construct individual classifiers. Ensemble learning strategies of these classifiers were also experimented with. The best performing machine learning models achieved 0.76 AUC for early stillbirth, 0.63 for late stillbirth and 0.64 for preterm birth while using a external NYC test data. The repeatable performance of our models demonstrates robustness that is required in this context. Our proposed novel models provide a solid foundation for risk prediction and could be further improved with the addition of biochemical and/or biophysical markers. |
format | Online Article Text |
id | pubmed-7096343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-70963432020-03-27 Predicting risk of stillbirth and preterm pregnancies with machine learning Koivu, Aki Sairanen, Mikko Health Inf Sci Syst Research Modelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study, we utilize state-of-the-art machine learning methods in the task of predicting early stillbirth, late stillbirth and preterm birth pregnancies. The aim of this experimentation is to discover novel risk models that could be utilized in a clinical setting. A CDC data set of almost sixteen million observations was used conduct feature selection, parameter optimization and verification of proposed models. An additional NYC data set was used for external validation. Algorithms such as logistic regression, artificial neural network and gradient boosting decision tree were used to construct individual classifiers. Ensemble learning strategies of these classifiers were also experimented with. The best performing machine learning models achieved 0.76 AUC for early stillbirth, 0.63 for late stillbirth and 0.64 for preterm birth while using a external NYC test data. The repeatable performance of our models demonstrates robustness that is required in this context. Our proposed novel models provide a solid foundation for risk prediction and could be further improved with the addition of biochemical and/or biophysical markers. Springer International Publishing 2020-03-25 /pmc/articles/PMC7096343/ /pubmed/32226625 http://dx.doi.org/10.1007/s13755-020-00105-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Koivu, Aki Sairanen, Mikko Predicting risk of stillbirth and preterm pregnancies with machine learning |
title | Predicting risk of stillbirth and preterm pregnancies with machine learning |
title_full | Predicting risk of stillbirth and preterm pregnancies with machine learning |
title_fullStr | Predicting risk of stillbirth and preterm pregnancies with machine learning |
title_full_unstemmed | Predicting risk of stillbirth and preterm pregnancies with machine learning |
title_short | Predicting risk of stillbirth and preterm pregnancies with machine learning |
title_sort | predicting risk of stillbirth and preterm pregnancies with machine learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7096343/ https://www.ncbi.nlm.nih.gov/pubmed/32226625 http://dx.doi.org/10.1007/s13755-020-00105-9 |
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