TY -的AU -库马尔,Mukkesh AU - Ang,李Ting AU - Ho,辛迪盟——Soh蜀E AU - Tan, Kok大华AU - Chan杰瑞Kok日圆AU -戈弗雷,基思米非盟- Chan Shiao-Yng AU - Chong, Yap Seng AU -埃里克森,约翰G盟——冯Mengling盟,Karnani Neerja PY - 2022 DA - 2022/7/5 TI -机Learning-Derived产前预测风险模型指导干预和预防妊娠糖尿病2型糖尿病的进展:预测模型开发研究乔- JMIR糖尿病SP - e32366六世- 7 - 3 KW -亚洲人口KW -糖尿病管理KW -数字医疗KW -妊娠期糖尿病KW -机器学习KW -预测模型KW -产前护理KW -公共卫生KW -风险因素KW - AB -背景:2型糖尿病患病率的增加妊娠糖尿病(GDM)是关于女性与GDM 2型糖尿病高危(T2D)在以后的生活中。这一风险的严重性凸显了早期干预预防GDM向T2D进展的重要性。产后筛查率并不理想，在亚洲国家通常只有13%。在一些卫生保健系统中，缺乏通过结构化产后筛查进行预防保健，公众意识不高是进行产后糖尿病筛查的主要障碍。目的:在本研究中，我们开发了一个机器学习模型，用于常规产前GDM筛查后产后T2D的早期预测。在产前护理中早期预测产后T2D将有助于实施有效的糖尿病预防干预策略。据我们所知，这是第一个在亚洲裔产前人群中使用机器学习进行产后T2D风险评估的研究。方法:前瞻性多民族数据(中国、马来和印度民族)来自新加坡最深入表型的母亲-后代队列研究-在新加坡成长走向健康结果-中的561例妊娠用于预测建模。特征变量包括人口统计学、病史或产科史、体格测量、生活方式信息和GDM诊断。 Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters. Results: A high performance prediction model for postpartum T2D comprising of 2 midgestation features—midpregnancy BMI after gestational weight gain and diagnosis of GDM—was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P=.02; OR 0.88, 95% CI 0.79-0.98). Conclusions: Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care. Trial Registration: ClinicalTrials.gov NCT01174875; https://clinicaltrials.gov/ct2/show/NCT01174875 SN - 2371-4379 UR - https://diabetes.www.mybigtv.com/2022/3/e32366 UR - https://doi.org/10.2196/32366 UR - http://www.ncbi.nlm.nih.gov/pubmed/35788016 DO - 10.2196/32366 ID - info:doi/10.2196/32366 ER -