KI Research Institute, Kfar Malal, Israel.
Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
BMC Pregnancy Childbirth. 2021 Sep 17;21(1):630. doi: 10.1186/s12884-021-04087-8.
Postpartum depression is a widespread disorder, adversely affecting the well-being of mothers and their newborns. We aim to utilize machine learning for predicting risk of postpartum depression (PPD) using primary care electronic health records (EHR) data, and to evaluate the potential value of EHR-based prediction in improving the accuracy of PPD screening and in early identification of women at risk.
We analyzed EHR data of 266,544 women from the UK who gave first live birth between 2000 and 2017. We extracted a multitude of socio-demographic and medical variables and constructed a machine learning model that predicts the risk of PPD during the year following childbirth. We evaluated the model's performance using multiple validation methodologies and measured its accuracy as a stand-alone tool and as an adjunct to the standard questionnaire-based screening by Edinburgh postnatal depression scale (EPDS).
The prevalence of PPD in the analyzed cohort was 13.4%. Combing EHR-based prediction with EPDS score increased the area under the receiver operator characteristics curve (AUC) from 0.805 to 0.844 and the sensitivity from 0.72 to 0.76, at specificity of 0.80. The AUC of the EHR-based prediction model alone varied from 0.72 to 0.74 and decreased by only 0.01-0.02 when applied as early as before the beginning of pregnancy.
PPD risk prediction using EHR data may provide a complementary quantitative and objective tool for PPD screening, allowing earlier (pre-pregnancy) and more accurate identification of women at risk, timely interventions and potentially improved outcomes for the mother and child.
产后抑郁症是一种广泛存在的疾病，会对母亲及其新生儿的健康产生不利影响。我们旨在利用机器学习，通过初级保健电子健康记录（EHR）数据预测产后抑郁症（PPD）的风险，并评估基于 EHR 的预测在提高 PPD 筛查准确性和早期识别风险妇女方面的潜在价值。
我们分析了来自英国的 266544 名首次分娩的女性的 EHR 数据，这些女性在 2000 年至 2017 年期间分娩。我们提取了大量社会人口统计学和医学变量，并构建了一个机器学习模型，预测产后一年内发生 PPD 的风险。我们使用多种验证方法评估了模型的性能，并测量了其作为独立工具和作为爱丁堡产后抑郁量表（EPDS）基于问卷的标准筛查的辅助工具的准确性。
在分析的队列中，PPD 的患病率为 13.4%。将基于 EHR 的预测与 EPDS 评分相结合，将接收者操作特征曲线下的面积（AUC）从 0.805 提高到 0.844，敏感性从 0.72 提高到 0.76，特异性为 0.80。仅基于 EHR 的预测模型的 AUC 从 0.72 到 0.74 不等，当应用于怀孕前早期时，仅降低了 0.01-0.02。
使用 EHR 数据进行 PPD 风险预测可能为 PPD 筛查提供一种补充的定量和客观工具，从而更早（怀孕前）更准确地识别风险妇女，及时进行干预，并可能改善母婴结局。
