Electronic Health Records to Predict Gestational Diabetes Risk

被引：3
|
作者：
Mateen, Bilal A. [1 ,2 ]
David, Anna L. [3 ]
Denaxas, Spiros [2 ,4 ,5 ,6 ,7 ]
机构：
[1] Kings Coll Hosp London, London, England
[2] Alan Turing Inst, London, England
[3] UCL, Elizabeth Garrett Anderson Inst Womens Hlth, London, England
[4] UCL, Inst Hlth Informat, London, England
[5] Hlth Data Res UK, London, England
[6] UCL, Natl Inst Hlth Res Univ Coll London Hosp, Biomed Res Ctr, London, England
[7] UCL, British Heart Fdn Res Accelerator, London, England
来源：
TRENDS IN PHARMACOLOGICAL SCIENCES | 2020年 / 41卷 / 05期
基金：
英国工程与自然科学研究理事会;
关键词：
D O I：
10.1016/j.tips.2020.03.003
中图分类号：
R9 [药学];
学科分类号：
1007 ;
摘要：
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.
引用
收藏
页码：301 / 304
页数：5
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