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<p>Despite its centrality to women’s health, the menstrual cycle remains understudied in computational health research due to its complexity, variability, and limited data availability. Recent advances in generative artificial intelligence (AI) offer new opportunities for modeling large-scale, user-generated menstrual health data. We introduce and evaluate a generative foundation model trained on self-tracked data from over 1.2 million users of a widely used menstrual tracking app. We assess the model’s ability to generate physiologically plausible synthetic cycles and realistic tracking behaviors, examine whether learned representations capture meaningful temporal and symptomatic patterns, and evaluate privacy risks. Results show that the model produces high-fidelity synthetic data closely mirroring real-world users, with no evidence of data leakage, while learned representations consistently outperform baseline methods on downstream forecasting tasks. These findings highlight generative AI’s potential to advance menstrual health forecasting, support privacy-sensitive data sharing, and enable scientific inquiry in women’s health research.</p>

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A foundation model for capturing complexity of menstrual health data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月28日

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  • Robin Linzmayer,
  • Chao Pang,
  • Iñigo Urteaga,
  • Gamze Gürsoy,
  • Amanda A. Shea,
  • Virginia J. Vitzthum,
  • Noémie Elhadad

摘要

Despite its centrality to women’s health, the menstrual cycle remains understudied in computational health research due to its complexity, variability, and limited data availability. Recent advances in generative artificial intelligence (AI) offer new opportunities for modeling large-scale, user-generated menstrual health data. We introduce and evaluate a generative foundation model trained on self-tracked data from over 1.2 million users of a widely used menstrual tracking app. We assess the model’s ability to generate physiologically plausible synthetic cycles and realistic tracking behaviors, examine whether learned representations capture meaningful temporal and symptomatic patterns, and evaluate privacy risks. Results show that the model produces high-fidelity synthetic data closely mirroring real-world users, with no evidence of data leakage, while learned representations consistently outperform baseline methods on downstream forecasting tasks. These findings highlight generative AI’s potential to advance menstrual health forecasting, support privacy-sensitive data sharing, and enable scientific inquiry in women’s health research.