Research

Using machine learning to predict the risk of developing gestational diabetes using a contemporary cohort

Abstract

Authors
Authors
Authors
Adesh Kadambi
https://www.delfina.com/resource/conference-predict-gdm

Authors: Adesh Kadambi, Isabel Fulcher, Jonathan Schor, Ali Ebrahim, Senan Ebrahim, Timothy Wen

Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2023

Key Findings: GDM can be predicted with moderate discrimination using machine learning models trained on nulliparous patient available at the first prenatal visit. Our model outperforms the current early GDM screening parameters and can ultimately assist screening to predict a diagnosis for earlier management.

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Research

Using machine learning to predict the risk of developing gestational diabetes using a contemporary cohort

Abstract

Authors
Authors
Authors
Adesh Kadambi
https://www.delfina.com/resource/conference-predict-gdm

Authors: Adesh Kadambi, Isabel Fulcher, Jonathan Schor, Ali Ebrahim, Senan Ebrahim, Timothy Wen

Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2023

Key Findings: GDM can be predicted with moderate discrimination using machine learning models trained on nulliparous patient available at the first prenatal visit. Our model outperforms the current early GDM screening parameters and can ultimately assist screening to predict a diagnosis for earlier management.

READ NOW

Research

Using machine learning to predict the risk of developing gestational diabetes using a contemporary cohort

Abstract

Authors
Authors
Authors
Adesh Kadambi
https://www.delfina.com/resource/conference-predict-gdm

Authors: Adesh Kadambi, Isabel Fulcher, Jonathan Schor, Ali Ebrahim, Senan Ebrahim, Timothy Wen

Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2023

Key Findings: GDM can be predicted with moderate discrimination using machine learning models trained on nulliparous patient available at the first prenatal visit. Our model outperforms the current early GDM screening parameters and can ultimately assist screening to predict a diagnosis for earlier management.

READ NOW

Research

Using machine learning to predict the risk of developing gestational diabetes using a contemporary cohort

Abstract

Authors
Authors
Authors
Adesh Kadambi
https://www.delfina.com/resource/conference-predict-gdm

Authors: Adesh Kadambi, Isabel Fulcher, Jonathan Schor, Ali Ebrahim, Senan Ebrahim, Timothy Wen

Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2023

Key Findings: GDM can be predicted with moderate discrimination using machine learning models trained on nulliparous patient available at the first prenatal visit. Our model outperforms the current early GDM screening parameters and can ultimately assist screening to predict a diagnosis for earlier management.

READ NOW

Research

Using machine learning to predict the risk of developing gestational diabetes using a contemporary cohort

Abstract

https://www.delfina.com/resource/conference-predict-gdm

Authors: Adesh Kadambi, Isabel Fulcher, Jonathan Schor, Ali Ebrahim, Senan Ebrahim, Timothy Wen

Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2023

Key Findings: GDM can be predicted with moderate discrimination using machine learning models trained on nulliparous patient available at the first prenatal visit. Our model outperforms the current early GDM screening parameters and can ultimately assist screening to predict a diagnosis for earlier management.

READ NOW

Research

Using machine learning to predict the risk of developing gestational diabetes using a contemporary cohort

Abstract

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https://www.delfina.com/resource/conference-predict-gdm