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Electronic Health Records to Predict Gestational Diabetes Risk

Mateen, BA; David, AL; Denaxas, S; (2020) Electronic Health Records to Predict Gestational Diabetes Risk. Trends in Pharmacological Sciences , 41 (5) pp. 301-304. 10.1016/j.tips.2020.03.003. Green open access

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Abstract

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.

Type: Article
Title: Electronic Health Records to Predict Gestational Diabetes Risk
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.tips.2020.03.003
Publisher version: https://doi.org/10.1016/j.tips.2020.03.003
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: machine learning, gestational diabetes mellitus, risk prediction, electronic health records, artificial intelligence
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine
URI: https://discovery.ucl.ac.uk/id/eprint/10097090
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