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.

本研究致力于通过机器学习实现女性激素周期的个性化建模，包括健康人和患有生殖系统疾病的人。通过使用高斯过程回归等技术，实现对个体周期相异性的建模并预测其周期阶段。结果表明，高斯过程可以有效地用来模拟女性激素周期，并有助于更好地理解和研究女性生殖健康领域。

基于机理模型和高斯过程的个性化女性激素周期建模实验