  In this paper, we introduce a novel task for machine learning in healthcare,
namely personalized modeling of the female hormonal cycle. The motivation for
this work is to model the hormonal cycle and predict its phases in time, both
for healthy individuals and for those with disorders of the reproductive
system. Because there are individual differences in the menstrual cycle, we are
particularly interested in personalized models that can account for individual
idiosyncracies, towards identifying phenotypes of menstrual cycles. As a first
step, we consider the hormonal cycle as a set of observations through time. We
use a previously validated mechanistic model to generate realistic hormonal
patterns, and experiment with Gaussian process regression to estimate their
values over time. Specifically, we are interested in the feasibility of
predicting menstrual cycle phases under varying learning conditions: number of
cycles used for training, hormonal measurement noise and sampling rates, and
informed vs. agnostic sampling of hormonal measurements. Our results indicate
that Gaussian processes can help model the female menstrual cycle. We discuss
the implications of our experiments in the context of modeling the female
menstrual cycle.
