A machine-learning tool has been developed to predict individuals at high risk of developing post-partum depression, a condition that affects around 17% of people giving birth worldwide. The tool uses electronic health records and post-partum-depression screening scores to identify parents who would benefit most from preventive care. Researchers hope that this tool will help develop strategies to prevent depression in high-risk individuals.
Forecast for 6 months: Within the next 6 months, we can expect to see the implementation of the machine-learning tool in several hospitals and healthcare systems, allowing for early identification and intervention of high-risk individuals.
Forecast for 1 year: By the end of the year, we can anticipate a significant reduction in post-partum depression cases among high-risk individuals, thanks to the proactive preventive measures implemented using the machine-learning tool.
Forecast for 5 years: In the next 5 years, the machine-learning tool is expected to become a standard practice in obstetric care, leading to a significant decrease in post-partum depression cases and improved mental health outcomes for new mothers.
Forecast for 10 years: By the end of the decade, post-partum depression is expected to be a rare condition, thanks to the widespread adoption of the machine-learning tool and the development of more effective preventive strategies.
