Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics. Urteaga, I., Bertin, T., Hardy, T. M., Albers, D. J., & Elhadad, N. In Proceedings of the 4th Machine Learning for Healthcare, volume 106, of Proceedings of Machine Learning Research, pages 66–90, 09–10 Aug, 2019. PMLR.
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics [link]Paper  abstract   bibtex   
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.
@InProceedings{ip-Urteaga2019,
  author    = {I{\~{n}}igo Urteaga and Tristan Bertin and Theresa M. Hardy and David J. Albers and No{\'{e}}mie Elhadad},
  title     = {{Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics}},
  booktitle = {Proceedings of the 4th Machine Learning for Healthcare},
  year      = {2019},
  volume    = {106},
  series    = {Proceedings of Machine Learning Research},
  pages     = {66--90},
  month     = {09--10 Aug},
  publisher = {PMLR},
  abstract  = {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.},
  file      = {urteaga19a.pdf:http\://proceedings.mlr.press/v106/urteaga19a/urteaga19a.pdf:PDF},
  url       = {http://proceedings.mlr.press/v106/urteaga19a.html},
}
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