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

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Machine learning accurately predicts first-time depression in pregnant individuals using early pregnancy self-reported data. Including food insecurity improved model accuracy and simplicity, highlighting its importance.

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Area of Science:

  • Perinatal mental health research
  • Machine learning applications in healthcare
  • Predictive modeling for maternal well-being

Background:

  • Depression during pregnancy is a significant concern.
  • Early identification of at-risk individuals is crucial for timely intervention.
  • Patient-reported data offers a valuable resource for predictive health models.

Purpose of the Study:

  • To develop a machine learning algorithm for predicting first-time moderate-to-severe depression in pregnant individuals.
  • Utilize patient-reported data collected during early pregnancy.
  • Enhance early detection and intervention strategies for perinatal depression.

Main Methods:

  • A cohort of 944 pregnant participants used a mobile app for data collection (September 2019 - April 2022).
  • Self-reported clinical and social risk factors were collected in the first trimester.
  • Machine learning algorithms, including causal discovery, were applied to 80/20 split training/test datasets.

Main Results:

  • Models accurately predicted depression with AUCs ranging from 0.74-0.89.
  • Key predictors included anxiety history, partnered status, psychosocial factors, and pregnancy stressors.
  • An optimal model with 14 variables achieved AUC 0.89; incorporating food insecurity further improved it to AUC 0.93 with 9 variables.

Conclusions:

  • A concise set of self-reported data can create a highly predictive model for first-time depression in pregnant individuals.
  • Food insecurity emerged as a critical factor, simplifying the model and enhancing its predictive power.
  • This approach holds promise for early identification and prevention of perinatal depression.