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

ABSTRACT:

Background

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.

Methods

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 mea

SUBMITTER: Amit G 

PROVIDER: S-EPMC8447665 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature