Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

Abstract The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predic...

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Auteurs principaux: Takafumi Yamauchi, Daisuke Ochi, Naomi Matsukawa, Daisuke Saigusa, Mami Ishikuro, Taku Obara, Yoshiki Tsunemoto, Satsuki Kumatani, Riu Yamashita, Osamu Tanabe, Naoko Minegishi, Seizo Koshiba, Hirohito Metoki, Shinichi Kuriyama, Nobuo Yaegashi, Masayuki Yamamoto, Masao Nagasaki, Satoshi Hiyama, Junichi Sugawara
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Medicine
R
Science
Q
Accès en ligne:https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f
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Internet

https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f

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