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Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus
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Parkhi, Durga, Periyathambi, Nishanthi, Ghebremichael-Weldeselassie, Yonas, Patel, Vinod, Sukumar, Nithya, Siddharthan, Rahul, Narlikar, Leelavati and Saravanan, Ponnusamy (2023) Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus. iScience, 26 (10). 107846. doi:10.1016/j.isci.2023.107846 ISSN 2589-0042.
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Official URL: https://doi.org/10.1016/j.isci.2023.107846
Abstract
Early onset of type 2 diabetes and cardiovascular disease are common complications for women diagnosed with gestational diabetes. Prediabetes refers to a condition in which blood glucose levels are higher than normal, but not yet high enough to be diagnosed as type 2 diabetes. Currently, there is no accurate way of knowing which women with gestational diabetes are likely to develop postpartum prediabetes. This study aims to predict the risk of postpartum prediabetes in women diagnosed with gestational diabetes. Our sparse logistic regression approach selects only two variables - antenatal fasting glucose at OGTT and HbA1c soon after the diagnosis of GDM - as relevant, but gives an area under the receiver operating characteristic curve of 0.72, outperforming all other methods. We envision this to be a practical solution, which coupled with a targeted follow-up of high-risk women, could yield better cardiometabolic outcomes in women with a history of GDM. [Abstract copyright: © 2023 The Authors.]
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > Q Science (General) R Medicine > RC Internal medicine R Medicine > RG Gynecology and obstetrics |
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Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Population, Evidence & Technologies (PET) Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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SWORD Depositor: | Library Publications Router | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Pregnancy -- Complications, Diabetes in pregnancy, Diabetes in pregnancy -- Diagnosis -- Simulation methods, Prediabetic state , Machine learning | |||||||||||||||
Journal or Publication Title: | iScience | |||||||||||||||
Publisher: | Cell Press | |||||||||||||||
ISSN: | 2589-0042 | |||||||||||||||
Official Date: | 20 October 2023 | |||||||||||||||
Dates: |
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Volume: | 26 | |||||||||||||||
Number: | 10 | |||||||||||||||
Article Number: | 107846 | |||||||||||||||
DOI: | 10.1016/j.isci.2023.107846 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Re-use Statement: | ** From PubMed via Jisc Publications Router ** History: received 24-02-2023; revised 27-05-2023; accepted 05-09-2023. | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 7 November 2023 | |||||||||||||||
Date of first compliant Open Access: | 7 November 2023 | |||||||||||||||
RIOXX Funder/Project Grant: |
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