Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7SQ, UK.
Oxford-Suzhou Centre for Advanced Research, Suzhou 215000, China.
Sensors (Basel). 2022 Jun 25;22(13):4805. doi: 10.3390/s22134805.
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.
妊娠期糖尿病（GDM）通常在妊娠晚期诊断，这使得干预的时间窗口非常有限。然而，适当的评估、管理和治疗已被证明可以降低 GDM 的并发症风险。本研究提出了一种基于机器学习的分层系统，用于根据 GDM 患者的日常血糖测量和电子健康记录（EHR）数据，识别可能出现高血糖水平的患者。我们在牛津大学医院 NHS 基金会信托（OUH）的 1148 例妊娠队列中进行了内部培训和验证，并在皇家伯克郡医院 NHS 基金会信托（RBH）的 709 例患者中进行了外部验证。我们训练了线性和非线性树基回归模型来预测患者在未来几天内可能出现的高读数比例（读数高于英国国家卫生与保健优化研究所 [NICE] 指南），并发现 XGBoost 在内部验证期间表现最佳（MSE、R2、MAE 的分别为 0.021 [0.019-0.023]、0.482 [0.442-0.516] 和 0.112 [0.109-0.116]）。该模型在外部验证中也表现出类似的性能，表明我们的方法在不同的 GDM 患者队列中具有通用性。
