Machine Learning-Based Risk Stratification for Gestational Diabetes Management.
Machine Learning-Based Risk Stratification for Gestational Diabetes Management.
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DOI：
10.3390/s22134805
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发表时间：
2022-06-25
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妊娠糖尿病（GDM）通常在妊娠的最后三个月被诊断出来，只有很短的时间进行干预。然而，适当的评估，管理和治疗已被证明可以减少GDM的并发症。这项研究介绍了一种基于机器学习的分层系统，用于根据GDM患者的每日血糖测量和电子健康记录（EHR）数据识别具有高血糖水平风险的患者。我们在牛津大学医院NHS基金会信托基金（OUH）的1148名孕妇队列中对我们的模型进行了内部训练和验证，并对来自皇家伯克希尔医院NHS基金会信托基金（RBH）的709名患者进行了外部验证。我们训练了线性和非线性基于树的回归模型来预测高读数的比例（读数高于英国国家健康与护理卓越研究所[NICE]指南）患者可能会在未来几天内出现，并发现XGBoost在内部验证期间达到了最高性能（MSE、R2、MAE分别为0.021 [CI 0.019-0.023]、0.482 [0.442-0.516]和0.112 [0.109-0.116]）。该模型在外部验证过程中也表现相似，表明我们的方法在不同GDM患者队列中是可推广的。
Gestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention. However, appropriate assessment, management, and treatment have been shown to reduce the complications of GDM. This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health record (EHR) data from GDM patients. We internally trained and validated our model on a cohort of 1148 pregnancies at Oxford University Hospitals NHS Foundation Trust (OUH), and performed external validation on 709 patients from Royal Berkshire Hospital NHS Foundation Trust (RBH). We trained linear and non-linear tree-based regression models to predict the proportion of high-readings (readings above the UK’s National Institute for Health and Care Excellence [NICE] guideline) a patient may exhibit in upcoming days, and found that XGBoost achieved the highest performance during internal validation (0.021 [CI 0.019–0.023], 0.482 [0.442–0.516], and 0.112 [0.109–0.116], for MSE, R2, MAE, respectively). The model also performed similarly during external validation, suggesting that our method is generalizable across different cohorts of GDM patients.
DOI：
10.1007/5584_2020_552
发表时间：
2021-01-01
期刊：
DIABETES: FROM RESEARCH TO CLINICAL PRACTICE, VOL 4
影响因子：
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作者：
Oskovi-Kaplan, Z. Asli;Ozgu-Erdinc, A. Seval
通讯作者：
Ozgu-Erdinc, A. Seval
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5
作者：
Mackillop, Lucy;Loerup, Lise;Tarassenko, Lionel
通讯作者：
Tarassenko, Lionel
影响因子：
8.2
作者：
Bochkur Dratver MA;Arenas J;Thaweethai T;Yu C;James K;Rosenberg EA;Callahan MJ;Cayford M;Tangren JS;Bernstein SN;Hivert MF;Thadhani R;Powe CE
通讯作者：
Powe CE