Development and validation of a machine learning‐based postpartum depression prediction model: A nationwide cohort study

Abstract

Background Currently, postpartum depression (PPD) screening is mainly based on self‐report symptom‐based assessment, with lack of an objective, integrative tool which identifies women at increased risk, before the emergent of PPD. We developed and validated a machine learning‐based PPD prediction model utilizing electronic health record (EHR) data, and identified novel PPD predictors. Methods A nationwide longitudinal cohort that included 214,359 births between January 2008 and December 2015, divided into model training and validation sets, was constructed utilizing Israel largest health maintenance organization's EHR‐database. PPD was defined as new diagnosis of a depressive episode or antidepressant prescription within the first year postpartum. A gradient‐boosted decision tree algorithm was applied to EHR‐derived sociodemographic, clinical, and obstetric features. Results Among the birth cohort, 1.9% (n = 4104) met the case definition of new‐onset PPD. In the validation set, the prediction model achieved an area under the curve (AUC) of 0.712 (95% confidence interval, 0.690–0.733), with a sensitivity of 0.349 and a specificity of 0.905 at the 90th percentile risk threshold, identifying PPDs at a rate more than three times higher than the overall set (positive and negative predictive values were 0.074 and 0.985, respectively). The model's strongest predictors included both well‐recognized (e.g., past depression) and less‐recognized (differing patterns of blood tests) PPD risk factors. Conclusions Machine learning‐based models incorporating EHR‐derived predictors, could augment symptom‐based screening practice by identifying the high‐risk population at greatest need for preventive intervention, before development of PPD.

Estimation of postpartum depression risk from electronic health records using machine learning

“…It should be noted that these associations do not imply casual effects between the predictor variables and the outcome. Our results are consistent with a recent study [17] that reported an AUC of 0.71 for an EHR-based PPD prediction model applied to an Israeli cohort of 214K women with PPD prevalence of 1.9%. Our cohort had significantly higher prevalence of PPD (13.4%) and included only first live births.…”

“…This limits the availability of reliable outcome measures and may introduce biases. Recently, a PPD prediction model using primary care EHR was reported to achieve AUC of 0.71 on a nationwide cohort [17]. The rate of PPD in this cohort was 1.9%, which is lower than the estimated population level prevalence.…”

Depression, Anxiety and Stress in Women with Breech Pregnancy - "Be not Afraid": A Prospective Study

“…Indeed, prenatal screening for anxiety can be implemented into prediction models used to earlier identify mothers and offspring at risk. [50]…”

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

“…Secondly, there is subjective deviation in the diagnosis of depression in clinic [ 5 ]. At present, the common method of the diagnosis of depression is mainly based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), which is subjective by doctors’ interviews with patients and evaluation scales [ 6 , 7 ]. Thus, it is difficult to identify depression, and there are some missed diagnoses and misdiagnoses.…”