Predicting the risk of gestational diabetes using clinical data with machine learning: a predictive model study

Adesh Kadambi; Isabel Fulcher; Kartik Venkatesh; Jonathan S. Schor; Mark A. Clapp; Timothy Wen

doi:10.1016/j.ajogmf.2023.100965

Predicting the risk of gestational diabetes using clinical data with machine learning: a predictive model study

Adesh Kadambi
, Isabel Fulcher
, Kartik Venkatesh
, Jonathan S. Schor
, Mark A. Clapp
, Timothy Wen

Obstetrics & Gynecology

Research output: Contribution to journal › Letter › peer-review

1 Scopus citations

Original language	English
Article number	100965
Journal	American journal of obstetrics & gynecology MFM
Volume	5
Issue number	7
DOIs	https://doi.org/10.1016/j.ajogmf.2023.100965
State	Published - Jul 2023

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10.1016/j.ajogmf.2023.100965

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@article{e31a7006d61a47bfa718890d6fd343d4,

title = "Predicting the risk of gestational diabetes using clinical data with machine learning: a predictive model study",

author = "Adesh Kadambi and Isabel Fulcher and Kartik Venkatesh and Schor, \{Jonathan S.\} and Clapp, \{Mark A.\} and Timothy Wen",

note = "Funding Information: The parent study was supported by grant funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under grant numbers U10 HD063036 ; U10 HD063072 ; U10 HD063047 ; U10 HD063037 ; U10 HD063041 ; U10 HD063020 ; U10 HD063046 ; U10 HD063048 ; and U10 HD063053 . In addition, support was provided by the Clinical and Translational Science Institutes under grant numbers UL1TR001108 and UL1TR000153 . We acknowledge the NICHD Data and Specimen Hub for providing the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be data that were used for this research. ",

year = "2023",

month = jul,

doi = "10.1016/j.ajogmf.2023.100965",

language = "English",

volume = "5",

journal = "American journal of obstetrics \& gynecology MFM",

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TY - JOUR

T1 - Predicting the risk of gestational diabetes using clinical data with machine learning

T2 - a predictive model study

AU - Kadambi, Adesh

AU - Fulcher, Isabel

AU - Venkatesh, Kartik

AU - Schor, Jonathan S.

AU - Clapp, Mark A.

AU - Wen, Timothy

N1 - Funding Information: The parent study was supported by grant funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under grant numbers U10 HD063036 ; U10 HD063072 ; U10 HD063047 ; U10 HD063037 ; U10 HD063041 ; U10 HD063020 ; U10 HD063046 ; U10 HD063048 ; and U10 HD063053 . In addition, support was provided by the Clinical and Translational Science Institutes under grant numbers UL1TR001108 and UL1TR000153 . We acknowledge the NICHD Data and Specimen Hub for providing the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be data that were used for this research.

PY - 2023/7

Y1 - 2023/7

UR - https://www.scopus.com/pages/publications/85158058937

U2 - 10.1016/j.ajogmf.2023.100965

DO - 10.1016/j.ajogmf.2023.100965

M3 - Letter

C2 - 37037325

AN - SCOPUS:85158058937

SN - 2589-9333

VL - 5

JO - American journal of obstetrics & gynecology MFM

JF - American journal of obstetrics & gynecology MFM

IS - 7

M1 - 100965

ER -

Predicting the risk of gestational diabetes using clinical data with machine learning: a predictive model study

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