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Association of Preterm Birth with Depression and Particulate Matter: Machine Learning Analysis Using National Health Insurance Data
This study uses machine learning and population data to analyze major determinants of preterm birth including depression and particulate matter. Retrospective cohort data came from Korea National Health Insurance Service claims data for 405,586 women who were aged 25–40 years and gave births for the...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003604/ https://www.ncbi.nlm.nih.gov/pubmed/33808913 http://dx.doi.org/10.3390/diagnostics11030555 |
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author | Lee, Kwang-Sig Kim, Hae-In Kim, Ho Yeon Cho, Geum Joon Hong, Soon Cheol Oh, Min Jeong Kim, Hai Joong Ahn, Ki Hoon |
author_facet | Lee, Kwang-Sig Kim, Hae-In Kim, Ho Yeon Cho, Geum Joon Hong, Soon Cheol Oh, Min Jeong Kim, Hai Joong Ahn, Ki Hoon |
author_sort | Lee, Kwang-Sig |
collection | PubMed |
description | This study uses machine learning and population data to analyze major determinants of preterm birth including depression and particulate matter. Retrospective cohort data came from Korea National Health Insurance Service claims data for 405,586 women who were aged 25–40 years and gave births for the first time after a singleton pregnancy during 2015–2017. The dependent variable was preterm birth during 2015–2017 and 90 independent variables were included (demographic/socioeconomic information, particulate matter, disease information, medication history, obstetric information). Random forest variable importance was used to identify major determinants of preterm birth including depression and particulate matter. Based on random forest variable importance, the top 40 determinants of preterm birth during 2015–2017 included socioeconomic status, age, proton pump inhibitor, benzodiazepine, tricyclic antidepressant, sleeping pills, progesterone, gastroesophageal reflux disease (GERD) for the years 2002–2014, particulate matter for the months January–December 2014, region, myoma uteri, diabetes for the years 2013–2014 and depression for the years 2011–2014. In conclusion, preterm birth has strong associations with depression and particulate matter. What is really needed for effective prenatal care is strong intervention for particulate matters together with active counseling and medication for common depressive symptoms (neglected by pregnant women). |
format | Online Article Text |
id | pubmed-8003604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80036042021-03-28 Association of Preterm Birth with Depression and Particulate Matter: Machine Learning Analysis Using National Health Insurance Data Lee, Kwang-Sig Kim, Hae-In Kim, Ho Yeon Cho, Geum Joon Hong, Soon Cheol Oh, Min Jeong Kim, Hai Joong Ahn, Ki Hoon Diagnostics (Basel) Article This study uses machine learning and population data to analyze major determinants of preterm birth including depression and particulate matter. Retrospective cohort data came from Korea National Health Insurance Service claims data for 405,586 women who were aged 25–40 years and gave births for the first time after a singleton pregnancy during 2015–2017. The dependent variable was preterm birth during 2015–2017 and 90 independent variables were included (demographic/socioeconomic information, particulate matter, disease information, medication history, obstetric information). Random forest variable importance was used to identify major determinants of preterm birth including depression and particulate matter. Based on random forest variable importance, the top 40 determinants of preterm birth during 2015–2017 included socioeconomic status, age, proton pump inhibitor, benzodiazepine, tricyclic antidepressant, sleeping pills, progesterone, gastroesophageal reflux disease (GERD) for the years 2002–2014, particulate matter for the months January–December 2014, region, myoma uteri, diabetes for the years 2013–2014 and depression for the years 2011–2014. In conclusion, preterm birth has strong associations with depression and particulate matter. What is really needed for effective prenatal care is strong intervention for particulate matters together with active counseling and medication for common depressive symptoms (neglected by pregnant women). MDPI 2021-03-19 /pmc/articles/PMC8003604/ /pubmed/33808913 http://dx.doi.org/10.3390/diagnostics11030555 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Lee, Kwang-Sig Kim, Hae-In Kim, Ho Yeon Cho, Geum Joon Hong, Soon Cheol Oh, Min Jeong Kim, Hai Joong Ahn, Ki Hoon Association of Preterm Birth with Depression and Particulate Matter: Machine Learning Analysis Using National Health Insurance Data |
title | Association of Preterm Birth with Depression and Particulate Matter: Machine Learning Analysis Using National Health Insurance Data |
title_full | Association of Preterm Birth with Depression and Particulate Matter: Machine Learning Analysis Using National Health Insurance Data |
title_fullStr | Association of Preterm Birth with Depression and Particulate Matter: Machine Learning Analysis Using National Health Insurance Data |
title_full_unstemmed | Association of Preterm Birth with Depression and Particulate Matter: Machine Learning Analysis Using National Health Insurance Data |
title_short | Association of Preterm Birth with Depression and Particulate Matter: Machine Learning Analysis Using National Health Insurance Data |
title_sort | association of preterm birth with depression and particulate matter: machine learning analysis using national health insurance data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003604/ https://www.ncbi.nlm.nih.gov/pubmed/33808913 http://dx.doi.org/10.3390/diagnostics11030555 |
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