The forecasting of menstruation based on a state-space modeling of basal body temperature time series.

ABSTRACT: Women's basal body temperature (BBT) shows a periodic pattern that associates with menstrual cycle. Although this fact suggests a possibility that daily BBT time series can be useful for estimating the underlying phase state as well as for predicting the length of current menstrual cycle, little attention has been paid to model BBT time series. In this study, we propose a state-space model that involves the menstrual phase as a latent state variable to explain the daily fluctuation of BBT and the menstruation cycle length. Conditional distributions of the phase are obtained by using sequential Bayesian filtering techniques. A predictive distribution of the next menstruation day can be derived based on this conditional distribution and the model, leading to a novel statistical framework tha

SUBMITTER: Fukaya K 

PROVIDER: S-EPMC5575519 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature