Machine learning derived prenatal predictive risk model to guide intervention and prevent the progression of gestational diabetes mellitus to Type 2 Diabetes.
Machine learning derived prenatal predictive risk model to guide intervention and prevent the progression of gestational diabetes mellitus to Type 2 Diabetes.
Kumar, Mukkesh
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Ang, Li Ting
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Ho, Cindy
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Soh, S.E.
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Tan, Kok Hian
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Chan, Jerry K.Y.
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Godfrey, Keith
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Chan, Shiao-Yng
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Chong, Yap-Seng
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Eriksson, Johan G.
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Feng, Mengling
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Karmani, Neerja
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Kumar, Mukkesh
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Ang, Li Ting
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Ho, Cindy
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Soh, S.E.
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Tan, Kok Hian
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Chan, Jerry K.Y.
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Godfrey, Keith
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Chan, Shiao-Yng
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Chong, Yap-Seng
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Eriksson, Johan G.
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Feng, Mengling
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Karmani, Neerja
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Kumar, Mukkesh, Ang, Li Ting, Ho, Cindy, Soh, S.E., Tan, Kok Hian, Chan, Jerry K.Y., Godfrey, Keith, Chan, Shiao-Yng, Chong, Yap-Seng, Eriksson, Johan G., Feng, Mengling and Karmani, Neerja
(2022)
Machine learning derived prenatal predictive risk model to guide intervention and prevent the progression of gestational diabetes mellitus to Type 2 Diabetes.
JMIR Diabetes.
(In Press)
Text
Manuscript & Tables_20220104
- Accepted Manuscript
More information
Accepted/In Press date: 22 March 2022
Identifiers
Local EPrints ID: 456392
URI: http://eprints.soton.ac.uk/id/eprint/456392
PURE UUID: 6b6b4745-f18e-4b64-8926-e7c455f61f34
Catalogue record
Date deposited: 27 Apr 2022 15:41
Last modified: 17 Mar 2024 02:38
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Contributors
Author:
Mukkesh Kumar
Author:
Li Ting Ang
Author:
Cindy Ho
Author:
S.E. Soh
Author:
Kok Hian Tan
Author:
Jerry K.Y. Chan
Author:
Shiao-Yng Chan
Author:
Yap-Seng Chong
Author:
Johan G. Eriksson
Author:
Mengling Feng
Author:
Neerja Karmani
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