Estimation of postpartum depression risk from electronic health records using machine learning.
Estimation of postpartum depression risk from electronic health records using machine learning.
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DOI：
10.1186/s12884-021-04087-8
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发表时间：
2021-09-17
影响因子：
3.1
通讯作者：
Akiva P
中科院分区：
文献类型：
--
作者：
Amit G;Girshovitz I;Marcus K;Zhang Y;Pathak J;Bar V;Akiva P
产后抑郁症是一种普遍的疾病，对母亲及其新生儿的健康产生不利影响。我们的目标是利用机器学习来预测产后抑郁症（PPD）的风险，使用初级保健电子健康记录（EHR）数据，并评估基于EHR的预测在提高PPD筛查的准确性和早期识别高危女性方面的潜在价值。我们分析了2000年至2017年期间来自英国的266，544名女性的EHR数据。我们提取了大量社会人口统计和医学变量，并构建了一个机器学习模型，可以预测产后一年内患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的准确性，以及作为爱丁堡产后抑郁量表（EPDS）的标准量表筛选的辅助工具。在分析的队列中，PPD的患病率为13.4%。结合EHR预测与EPDS评分的受试者工作特征曲线下面积（AUC）从0.805增加到0.844，敏感性从0.72增加到0.76，特异性为0.80。基于EHR的预测模型的AUC从0.72到0.74不等，当早在怀孕开始前应用时仅下降0.01-0.02。使用EHR数据进行PPD风险预测可以为PPD筛查提供补充的定量和客观工具，允许更早（孕前）和更准确地识别处于风险中的妇女，及时干预并可能改善母亲和儿童的结局。在线版本包含补充材料，可通过10.1186/s12884-021-04087-8获得。
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. The online version contains supplementary material available at 10.1186/s12884-021-04087-8.
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影响因子：
3.8
作者：
Kozhimannil, Katy Backes;Trinacty, Connie Mah;Adams, Alyce S.
通讯作者：
Adams, Alyce S.
影响因子：
0.5
作者：
Kroenke, K;Spitzer, RL
通讯作者：
Spitzer, RL
影响因子：
7.2
作者：
Howell, EA;Mora, PA;Leventhal, H
通讯作者：
Leventhal, H
影响因子：
3.8
作者：
Forder, Peta M.;Rich, Jane;Loxton, Deborah
通讯作者：
Loxton, Deborah
影响因子：
64.3
作者：
Putnam, Karen T.;Wilcox, Marsha;Meltzer-Brody, Samantha
通讯作者：
Meltzer-Brody, Samantha