Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women.

ABSTRACT:

Objective

There is a scarcity in tools to predict postpartum depression (PPD). We propose a machine learning framework for PPD risk prediction using data extracted from electronic health records (EHRs).

Methods

Two EHR datasets containing data on 15,197 women from 2015 to 2018 at a single site, and 53,972 women from 2004 to 2017 at multiple sites were used as development and validation sets, respectively, to construct the PPD risk prediction model. The primary outcome was a diagnosis of PPD within 1 year following childbirth. A framework of data extraction, processing, and machine learning was implemented to select a minimal list of features from the EHR datasets to ensure model performance and to enable future point-of-care risk prediction.

Results

The best-performin

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC7738412 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature