Algorithm developed at the University of Pittsburgh predicts depression during pregnancy

Researchers at the University of Pittsburgh have developed a machine-learning algorithm that can predict with high accuracy which pregnant women, with no history of depression, are likely to develop depression in the second or third trimesters. Key predictors of depression include worries about financial stability, running out of food, managing ongoing health problems, stress about labor and delivery, and concerns about how the new baby might affect interpersonal relationships. Early identification of at-risk individuals can lead to timely preventive care options such as therapy, peer support, and tangible support like providing meals or reducing major stresses in their lives. This tool is the first of its kind to predict depression before it manifests, and the researchers are working on creating a screener for healthcare providers to identify pregnant women who may develop depression and provide them with necessary support.