2019/08/27 by Iñigo Urteaga, Urteaga, Iñigo, Tristan Bertin +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Genetic and phenotypic traits in livestock #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.AP #stat.ML
paper · pdf · doi:10.48550/arxiv.1908.10226
Accepted and presented in Machine Learning for Healthcare 2019
arxiv created 2019/08/27 · openalex publication_date 2019/08/27 · arxiv updated 2019/08/28 · openalex created_date 2019/12/05 · openalex updated_date 2026/07/28
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 a challenging task, but a critical one to improve general understanding of the menstrual cycle and personalized detection of potential health issues. Our goal is to infer and forecast individual hormone daily levels over time, while accommodating pragmatic and minimally invasive measurement settings. To that end, our approach combines the power of probabilistic generative models (i.e., multi-task Gaussian processes) with the flexibility of neural networks (i.e., a dilated convolutional architecture) to learn complex temporal mappings. To attain accurate hormone level reconstruction with as little data as possible, we propose a sampling mechanism for optimal reconstruction accuracy with limited sampling budget. Our results show the validity of our proposed hormonal dynamic modeling framework, as it provides accurate predictive performance across different realistic sampling budgets and outperforms baselines methods.