Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data.

ABSTRACT:

Purpose

To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression.

Methods

A sample of 944 U.S. patient participants from a larger longitudinal observational cohortused a prenatal support mobile app from September 2019 to April 2022. Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest

SUBMITTER: Krishnamurti T 

PROVIDER: S-EPMC11579171 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature