{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-personalized-modeling-of-the-female","title":"Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes","arxiv_id":"1712.00117","date":"2017-11-30","proceeding":null,"authors":["Iñigo Urteaga","David J. Albers","Marija Vlajic Wheeler","Anna Druet","Hans Raffauf","Noémie Elhadad"],"abstract":"In this paper, we introduce a novel task for machine learning in healthcare,\nnamely personalized modeling of the female hormonal cycle. The motivation for\nthis work is to model the hormonal cycle and predict its phases in time, both\nfor healthy individuals and for those with disorders of the reproductive\nsystem. Because there are individual differences in the menstrual cycle, we are\nparticularly interested in personalized models that can account for individual\nidiosyncracies, towards identifying phenotypes of menstrual cycles. As a first\nstep, we consider the hormonal cycle as a set of observations through time. We\nuse a previously validated mechanistic model to generate realistic hormonal\npatterns, and experiment with Gaussian process regression to estimate their\nvalues over time. Specifically, we are interested in the feasibility of\npredicting menstrual cycle phases under varying learning conditions: number of\ncycles used for training, hormonal measurement noise and sampling rates, and\ninformed vs. agnostic sampling of hormonal measurements. Our results indicate\nthat Gaussian processes can help model the female menstrual cycle. We discuss\nthe implications of our experiments in the context of modeling the female\nmenstrual cycle.","url_abs":"http://arxiv.org/abs/1712.00117v1","url_pdf":"http://arxiv.org/pdf/1712.00117v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-personalized-modeling-of-the-female","repo_url":"https://github.com/iurteaga/hmc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}