Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^1–4. GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^5,6. Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.

^{妊娠期糖尿病 (GDM) 会增加母亲和后代1-4}短期和长期并发症的风险。GDM 通常在妊娠 24-28 周时被诊断出来，但早期发现是可取的，因为这可以预防或显着降低不良妊娠结局的风险^5,6. 在这里，我们使用机器学习方法对以色列 588,622 名孕妇的回顾性数据预测 GDM，这些数据有全面的电子健康记录。我们的模型即使在妊娠开始时也能以高精度预测 GDM（受试者工作曲线下面积 (auROC) = 0.85），大大优于基线风险评分（auROC = 0.68）。我们在未来验证集和来自以色列人口最多的城市耶路撒冷的地理验证集上验证了我们的结果，从而模拟了现实世界的表现。询问我们的模型，我们发现了以前未报告的风险因素，包括以前怀孕葡萄糖挑战测试的结果。最后，我们设计了一个更简单的模型，该模型仅基于患者可以回答的九个问题，准确度仅适度降低（auROC = 0.80）。全面的，我们的模型可能允许对高风险女性进行早期干预，以及一种具有成本效益的筛查方法，可以通过识别低风险女性来避免对葡萄糖耐量测试的需要。未来需要对其他人群进行前瞻性研究和研究，以评估该模型的实际临床效用。