Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.

Al Ghadban, Yasmina; Du, Yuheng; Charnock-Jones, D Stephen; Garmire, Lana X; Smith, Gordon C S; Sovio, Ulla

Al Ghadban, Yasmina; Du, Yuheng; Charnock-Jones, D Stephen; Garmire, Lana X; Smith, Gordon C S; Sovio, Ulla.

Afiliação

Al Ghadban Y; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.
Du Y; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Charnock-Jones DS; Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK.
Garmire LX; NIHR Cambridge Biomedical Research Centre, Cambridge, UK.
Smith GCS; Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.
Sovio U; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

BJOG ; 2023 Nov 20.

Article em En | MEDLINE | ID: mdl-37984426

Palavras-chave

metabolomics; prediction; preterm; risk; spontaneous

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BJOG Assunto da revista: GINECOLOGIA / OBSTETRICIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido País de publicação: Reino Unido

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