Abstract
Globally, millions of women track their menstrual cycle and fertility via smartphone-based health apps, generating multivariate time series with frequent missing data. To leverage this type of data for studies of fertility or studies of the effect of the menstrual cycle on symptoms and diseases, it is critical to have methods for identifying reproductive events, such as ovulation, pregnancy losses or births. Here, we present a hierarchical approach relying on hidden semi-Markov models that adapts to changes in tracking behavior, explicitly captures variable- and state- dependent missingness, allows for variables of different type, and quantifies uncertainty. The accuracy on simulated data reaches 98% with no missing data and 90% with realistic missingness. On our partially labeled real-world time series, the accuracy reaches 93%. Our method also accurately predicts cycle length by learning user characteristics. Its implementation is publicly available (HiddenSemiMarkov R package) and transferable to any health time series, including self-reported symptoms and occasional tests.
Highlights
H EALTH tracking apps have become increasingly popular and self-reported health records collected via apps or connected devices are progressively adopted by the scientific community for personalized health or epidemiological research [1]
Menstrual cycle and fertility tracking apps are among the most used health apps [2]. These apps are used by millions of women worldwide, generating very large datasets of self-reports related to the menstrual cycle and reproductive events
We report the mean square error (MSE) between the predicted and the actual length of the fifth cycle and compare it with the MSE when using the average cycle length of the four previous cycle to predict the length of the fifth cycle
Summary
H EALTH tracking apps have become increasingly popular and self-reported health records collected via apps or connected devices are progressively adopted by the scientific community for personalized health or epidemiological research [1]. These apps are used by millions of women worldwide, generating very large datasets of self-reports related to the menstrual cycle and reproductive events Users of these apps typically report their period bleeding along with physical or psychological symptoms and/or fertility-related body-signs. These large datasets have already been used to characterize the duration of the menstrual cycle and the follicular (before ovulation) and luteal (after ovulation) phases [3]– [5], to evaluate the association between sexually transmittable infections (STI) and pre-menstrual symptoms [6] and to evaluate the association between cycle length irregularities and reported symptoms [7].
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