ArticleCNS spectrums2025
Beyond the hot flashes: how machine learning is uncovering the complexity of menopause-related depression.
Article in CNS spectrums, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe transition into menopause marks a significant stage in a woman's life, indicating the end of reproductive capability. This period, encompassing perimenopause and menopause, is characterized by declining levels of estrogen and progesterone, leading to various symptoms such as hot flashes, sleep disturbances, sexual dysfunction, and mood irregularities. Moreover, cognitive functions, notably memory, may decline during this phase.
objectiveThis exploratory study aimed to evaluate psychological factors in a sample of 98 women recruited from a diagnostic-assistance hospital pathway (AOUP).
methodsPsychological variables, including depression, anxiety, stress, sleep quality, memory, personality traits, and mindfulness, were assessed using psychometric questionnaires. Machine learning techniques were employed to identify independent variables strongly correlated with higher levels of depression measured by BDI-II.
resultsThe findings revealed positive associations between depression and anxiety, stress, low mood, poor sleep quality, and memory complaints, while mindfulness showed a negative correlation. Remarkably, the machine learning analysis achieved a high classification accuracy in distinguishing between individuals with different levels of depression (low vs high).
conclusionsThese results underscore the importance of addressing psychological factors during menopause and offer valuable insights for future research and the development of targeted clinical interventions aimed at enhancing mental health and quality of life for women during this transitional phase.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.