Artificial intelligence (AI) is positioned to have a major impact on virtually every sector, including healthcare. A new study suggests that machine learning models can identify women with severe subjective cognitive decline during the transition to menopause more quickly and affordably, effectively opening the door to better cognitive health management. The results of the study are published online today in Menopausethe magazine of The Menopause Society.
Subjective cognitive decline refers to a person’s perceived decline in memory or other cognitive functions. Cognitive decline, one of the most common symptoms associated with the transition to menopause, is of particular concern because it not only affects a woman’s quality of life but may also indicate a higher risk of serious neurodegenerative diseases, such as the disease of Alzheimer’s.
Previous evidence suggests a number of risk factors for cognitive decline, including aging, hypertension, obesity and depression, among others. One challenge is that most current cognitive health models focus on dementia, an incurable disease that offers limited scope for clinical intervention. Although subjective cognitive decline does not always predict long-term cognitive changes or dementia, a predictive model for cognitive decline and related factors could enable early intervention to protect cognitive health.
Existing tests for cognitive performance are largely based on models that typically include various laboratory indicators such as blood glucose, blood lipids, and brain imaging. The complexity and high cost of these models often make them impractical to implement in a clinical setting. In comparison, questionnaire-based models offer a simpler and more cost-effective alternative. These models are based on a number of independent variables, including sociodemographic, work-related, menstruation-related, lifestyle-related and mental health-related factors.
Machine learning has shown tremendous potential in the field of cognitive health in recent years. By mining patterns and trends from large data sets, it can construct accurate, reliable models and automate the handling of complex variable relationships. In this latest study of more than 1,200 women undergoing the transition to menopause, researchers were able to develop and validate a machine learning model for identifying women experiencing severe subjective cognitive decline, along with associated factors.
These findings provide new guidance for interventions designed to maintain cognitive health in women undergoing the transition to menopause. Additional research is needed to validate these results and identify additional potential influencing factors.
This study highlights how the use of machine learning can be used to identify women experiencing severe subjective cognitive decline during the transition to menopause and possible associated factors. Early identification of high-risk individuals may enable targeted interventions to protect cognitive health. Future studies with objective measures of cognition and longitudinal follow-up are crucial to better understand these associations.”
Dr. Stephanie Faubion, Medical Director, The Menopause Society
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Magazine reference:
Zhao, X., et al. (2025) Using machine learning models to identify severe subjective cognitive decline and related factors in nurses during the menopausal transition: a pilot study. Menopause. doi.org/10.1097/gme.0000000000002500.