Similarity-based Detection of Fertile Days at OvuFriend

Sosnowski, Łukasz; Chaber, Wojciech; Miłobędzki, Łukasz; Penza, Tomasz; Sosnowska, Jadwiga; Zaleski, Karol; Fedorowicz, Joanna; Szymusik, Iwona; Ślęzak, Dominik

doi:10.1109/bigdata.2018.8622602

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The menstrual cycle is a complex physiological process, and with access to more and more self-tracked data, computational research has very recently started to look into its modeling. Most previous work, however, has focused on high-level characteristics of the cycle, such as predicting cycle length or identifying phases based on specific signals such as body basal temperature (Scherwitzl et al, 2015;Sosnowski et al, 2018).…”

Section: Discussion and Related Workmentioning

confidence: 99%

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

Urteaga,

Bertin,

Hardy

et al. 2019

Preprint

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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 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.

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Section: Discussion and Related Workmentioning

confidence: 99%