AI Model Identifies Women with Subjective Cognitive Decline During Menopause: Daily Dose

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Patient Care brings primary care clinicians a lot of medical news every day—it’s easy to miss an important study. The Daily Dose provides a concise summary of one of the website's leading stories you may not have seen.

On January 16, 2025, we reported on a study published in the journal Menopause that was designed to examine a machine learning model for identifying women experiencing severe subjective cognitive decline (SCD) during the menopause transition.

The study

Researchers analyzed data from 1264 nurses aged 40 to 60 years experiencing the menopause transition. Participants completed detailed questionnaires covering demographic, occupational, and health-related variables, and SCD was assessed using the self-reported SCD-Q9 questionnaire. The questionnaire was condensed into 2 components – overall functional memory and time comparison and activities of daily living. A final score could range from 0 to 9, with higher scores reflecting greater severity. Nurses scoring a score of 7.5 or greater on the SCD-Q9 were categorized as having severe SCD, with 340 participants meeting this criterion.

Investigators trained the support vector machine (SVM) model to look for SCD by comparing the questionnaire results to other data from three-quarters of the nurses, including health, work life, menstrual and menopause symptoms, mental health, and lifestyle factors. They then tested the AI on the remaining quarter of the nurses to see if it could accurately detect SCD based on their data.

The findings

The SVM model outperformed 6 other machine learning algorithms in identifying individuals at high risk for severe SCD, achieving an area under the receiver operating characteristic curve of 0.846, an accuracy of 78.9%, and a specificity of 80.2%. Menopausal symptoms, stage of menopause, economic status, and sleep satisfaction emerged as the most influential predictors of severe SCD.

Authors' comments

“This research provides a framework for developing targeted interventions. By addressing modifiable factors such as sleep quality and emotional well-being, [health care] organizations can better support their workforce.”

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