Association of preterm birth with medications: machine learning analysis using national health insurance data-Reference-Cited by-同舟云学术

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Association of preterm birth with medications: machine learning analysis using national health insurance data

Published:2022-01-17 Issue:5 Volume:305 Page:1369-1376
ISSN:1432-0711
Container-title:Archives of Gynecology and Obstetrics
language:en
Short-container-title:Arch Gynecol Obstet

Author:

Lee Kwang-Sig^ORCID,Song In-Seok^ORCID,Kim Eun Sun^ORCID,Kim Hae-In,Ahn Ki Hoon^ORCID

Funder

Korea University Medical Center

Ministry of Science and ICT of South Korea

Publisher

Springer Science and Business Media LLC

Subject

Obstetrics and Gynecology,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00404-022-06405-7.pdf

Reference28 articles.

1. Blencowe H, Cousens S, Oestergaard MZ, Chou D, Moller AB, Narwal R et al (2012) National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet 379:2162–2172

2. El-Serag HB, Sweet S, Winchester CC, Dent J (2014) Update on the epidemiology of gastrooesophageal reflux disease: a systematic review. Gut 63:871–880

3. Kim N, Lee SW, Cho SI, Park CG, Yang CH, Kim HS et al (2008) The prevalence of and risk factors for erosive oesophagitis and non-erosive reflux disease: a nationwide multicentre prospective study in Korea. Aliment Pharmacol Ther 27:173–185

4. Tonetti MS, Greenwell H, Kornman KS (2018) Staging and grading of periodontitis: framework and proposal of a new classification and case definition. J Periodontol 89(Suppl 1):S159–S172

5. Kassebaum NJ, Bernabé E, Dahiya M, Bhandari B, Murray CJ, Marcenes W (2014) Global burden of severe periodontitis in 1990–2010: a systematic review and meta-regression. J Dent Res 93:1045–1053

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