Using Electronic Health Records and Machine Learning to Predict Postpartum Depression. Conference Paper uri icon

Overview

abstract

  • Postpartum depression (PPD) is one of the most frequent maternal morbidities after delivery with serious implications. Currently, there is a lack of effective screening strategies and high-quality clinical trials. The ability to leverage a large amount of detailed patient data from electronic health records (EHRs) to predict PPD could enable the implementation of effective clinical decision support interventions. To develop a PPD prediction model, using EHRs from Weill Cornell Medicine and NewYork-Presbyterian Hospital between 2015-17, 9,980 episodes of pregnancy were identified. Six machine learning algorithms, including L2-regularized Logistic Regression, Support Vector Machine, Decision Tree, Na├»ve Bayes, XGBoost, and Random forest were constructed. Our model's best prediction performance achieved an AUC of 0.79. Race, obesity, anxiety, depression, different types of pain, antidepressants, and anti-inflammatory drugs during pregnancy were among the significant predictors. Our results suggest a potential for applying machine learning to EHR data to predict PPD and inform healthcare delivery.

publication date

  • August 21, 2019

Research

keywords

  • Decision Support Systems, Clinical
  • Depression, Postpartum

Identity

Scopus Document Identifier

  • 85071478364

Digital Object Identifier (DOI)

  • 10.3233/SHTI190351

PubMed ID

  • 31438052

Additional Document Info

volume

  • 264