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In this paper, we introduce a novel task for machine learning in healthcare,namely personalized modeling of the female hormonal cycle. The motivation forthis work is to model the hormonal cycle and predict its phases in time, bothfor healthy individuals and for those with disorders of the reproductivesystem. Because there are individual differences in the menstrual cycle, we areparticularly interested in personalized models that can account for individualidiosyncracies, towards identifying phenotypes of menstrual cycles. As a firststep, we consider the hormonal cycle as a set of observations through time. Weuse a previously validated mechanistic model to generate realistic hormonalpatterns, and experiment with Gaussian process regression to estimate theirvalues over time. Specifically, we are interested in the feasibility ofpredicting menstrual cycle phases under varying learning conditions: number ofcycles used for training, hormonal measurement noise and sampling rates, andinformed vs. agnostic sampling of hormonal measurements. Our results indicatethat Gaussian processes can help model the female menstrual cycle. We discussthe implications of our experiments in the context of modeling the femalemenstrual cycle.

Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes