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We present an end-to-end statistical framework for personalized, accurate,and minimally invasive modeling of female reproductive hormonal patterns.Reconstructing and forecasting the evolution of hormonal dynamics is achallenging task, but a critical one to improve general understanding of themenstrual cycle and personalized detection of potential health issues. Our goalis to infer and forecast individual hormone daily levels over time, whileaccommodating pragmatic and minimally invasive measurement settings. To thatend, our approach combines the power of probabilistic generative models (i.e.,multi-task Gaussian processes) with the flexibility of neural networks (i.e., adilated convolutional architecture) to learn complex temporal mappings. Toattain accurate hormone level reconstruction with as little data as possible,we propose a sampling mechanism for optimal reconstruction accuracy withlimited sampling budget. Our results show the validity of our proposed hormonaldynamic modeling framework, as it provides accurate predictive performanceacross different realistic sampling budgets and outperforms baselines methods.

Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics