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Machine Learning Model Helps Identify Patients at Risk of Postpartum Depression
- Researchers led by Dr. Roy Perlis developed a machine-learning model in 2025 to predict postpartum depression risk using data from over 29,000 people in the United States.
- The model was created because current postpartum depression diagnosis relies mainly on subjective questionnaires and typically occurs weeks after delivery, delaying care.
- Using electronic health records at delivery, the tool integrates clinical and demographic data to identify individuals at elevated risk, excluding those with recent depression histories.
- The study found that parents identified as high risk by the model had approximately triple the chance of experiencing postpartum depression, with 30% of them developing the condition within six months after delivery.
- The model’s early risk identification could enable timely interventions like therapy and stress management, though further validation and clinical integration are ongoing priorities.
10 Articles
10 Articles
Machine learning model helps identify patients at risk of postpartum depression
Postpartum depression (PPD) affects up to 15 percent of individuals after childbirth. Early identification of patients at risk of PPD could improve proactive mental health support. Researchers developed a machine learning model that can evaluate patients' PPD risk using readily accessible clinical and demographic factors. Findings demonstrate the model's promising predictive capabilities.
MedPage Today
Anti-Aging, Acupuncture and Health NewsHistory in Siblings May Indicate Risk of Postpartum Psychosis
(MedPage Today) -- LOS ANGELES -- Pregnant women have a higher risk of postpartum psychosis if their sisters had the same condition, a Swedish cohort study suggested. The relative recurrence risk of postpartum psychosis for siblings adjusted...
New research improves risk prediction for postpartum depression and postpartum psychosis
The American Journal of Psychiatry published two new research papers on Monday, focused on identifying postpartum mental health issues earlier to improve outcomes and save lives.They were presented at the American Psychiatric Association's annual meeting in Los Angeles, California.The first research paper is from doctors at Mass General Brigham. It looks at postpartum depression, which affects up to 15% of women after childbirth. CLICK HERE to r…
Machine learning model predicts postpartum depression risk using health record data
Postpartum depression (PPD) affects up to 15% of individuals after childbirth. Early identification of patients at risk of PPD could improve proactive mental health support. Mass General Brigham researchers developed a machine learning model that can evaluate patients' PPD risk using readily accessible clinical and demographic factors. Findings demonstrating the model's promising predictive capabilities are published in the American Journal of P…
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