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.

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.

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