Electronic Health Records to Predict Gestational Diabetes Risk

Gestational diabetes mellitus is a common pregnancy complication associated with significant adverse health outcomes for both women and infants. Effective screening and early prediction tools as part of routine clinical care are needed to reduce the impact of the disease on the baby and mother. Usin...

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Published in:Trends in pharmacological sciences (Regular ed.) 2020-05, Vol.41 (5), p.301-304
Main Authors: Mateen, Bilal A., David, Anna L., Denaxas, Spiros
Format: Article
Language:English
Subjects:
Artificial Intelligence
Diabetes, Gestational - diagnosis
Diabetes, Gestational - epidemiology
Electronic Health Records
Female
gestational diabetes mellitus
Humans
machine learning
Mass Screening
Pregnancy
risk prediction
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description Gestational diabetes mellitus is a common pregnancy complication associated with significant adverse health outcomes for both women and infants. Effective screening and early prediction tools as part of routine clinical care are needed to reduce the impact of the disease on the baby and mother. Using large-scale electronic health records, Artzi and colleagues developed and evaluated a machine learning driven tool to identify women at high and low risk of GDM. Their findings showcase how artificial intelligence approaches can potentially be embedded in clinical care to enable accurate and rapid risk stratification.
doi_str_mv 10.1016/j.tips.2020.03.003
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subjects Artificial Intelligence
Diabetes, Gestational - diagnosis
Diabetes, Gestational - epidemiology
Electronic Health Records
Female
gestational diabetes mellitus
Humans
machine learning
Mass Screening
Pregnancy
risk prediction
title Electronic Health Records to Predict Gestational Diabetes Risk
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