The Experts below are selected from a list of 4236 Experts worldwide ranked by ideXlab platform
Makio Ishiguro - One of the best experts on this subject based on the ideXlab platform.
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a self excited threshold autoregressive state space model for menstrual cycles forecasting menstruation and identifying within cycle stages based on Basal Body Temperature
Statistics in Medicine, 2019Co-Authors: Ai Kawamori, Keiichi Fukaya, Masumi Kitazawa, Makio IshiguroAbstract:The menstrual cycle is divided into hypothermic and hyperthermic phases based on the periodic shift in the Basal Body Temperature (BBT), reflecting events occurring in the ovary. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for the BBT switch depending on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of the BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of hypothermic and hyperthermic phases, possibly as well as the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. The application of the proposed model to a large data set containing 25 622 cycles provided by 3533 women further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting its wide applicability.
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the forecasting of menstruation based on a state space modeling of Basal Body Temperature time series
Statistics in Medicine, 2017Co-Authors: Keiichi Fukaya, Ai Kawamori, Yutaka Osada, Masumi Kitazawa, Makio IshiguroAbstract: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 that provides a sequentially updated prediction for upcoming menstruation day. We applied this framework to a real data set of women's BBT and menstruation days and compared prediction accuracy of the proposed method with that of previous methods, showing that the proposed method generally provides a better prediction. Because BBT can be obtained with relatively small cost and effort, the proposed method can be useful for women's health management. Potential extensions of this framework as the basis of modeling and predicting events that are associated with the menstrual cycles are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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a self excited threshold autoregressive state space model for menstrual cycles forecasting menstruation and identifying ovarian phases based on Basal Body Temperature
arXiv: Applications, 2017Co-Authors: Ai Kawamori, Keiichi Fukaya, Masumi Kitazawa, Makio IshiguroAbstract:The menstrual cycle is composed of the follicular phase and subsequent luteal phase based on events occurring in the ovary. Basal Body Temperature (BBT) reflects this biphasic aspect of menstrual cycle and tends to be relatively low during the follicular phase. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for BBT switch depend on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of follicular and luteal phases, as well as to estimate the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. An application to a large data set containing 25,622 cycles provided by 3,533 woman subjects further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting wide applicability of the proposed model.
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the forecasting of menstruation based on a state space modeling of Basal Body Temperature time series
arXiv: Applications, 2016Co-Authors: Keiichi Fukaya, Ai Kawamori, Yutaka Osada, Masumi Kitazawa, Makio IshiguroAbstract:Women's Basal Body Temperature (BBT) follows a periodic pattern that is associated with the events in their menstrual cycle. Although daily BBT time series contain potentially useful information for estimating the underlying menstrual phase and for predicting the length of current menstrual cycle, few models have been constructed for BBT time series. Here, we propose a state-space model that includes menstrual phase as a latent state variable to explain fluctuations in BBT and menstrual cycle length. Conditional distributions for the menstrual phase were obtained by using sequential Bayesian filtering techniques. A predictive distribution for the upcoming onset of menstruation was then derived based on the conditional distributions and the model, leading to a novel statistical framework that provided a sequentially updated prediction of the day of onset of menstruation. We applied this framework to a real dataset comprising women's self-reported BBT and days of menstruation, comparing the prediction accuracy of our proposed method with that of conventional calendar calculation. We found that our proposed method provided a better prediction of the day of onset of menstruation. Potential extensions of this framework may provide the basis of modeling and predicting other events that are associated with the menstrual cycle.
Ai Kawamori - One of the best experts on this subject based on the ideXlab platform.
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a self excited threshold autoregressive state space model for menstrual cycles forecasting menstruation and identifying within cycle stages based on Basal Body Temperature
Statistics in Medicine, 2019Co-Authors: Ai Kawamori, Keiichi Fukaya, Masumi Kitazawa, Makio IshiguroAbstract:The menstrual cycle is divided into hypothermic and hyperthermic phases based on the periodic shift in the Basal Body Temperature (BBT), reflecting events occurring in the ovary. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for the BBT switch depending on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of the BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of hypothermic and hyperthermic phases, possibly as well as the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. The application of the proposed model to a large data set containing 25 622 cycles provided by 3533 women further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting its wide applicability.
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the forecasting of menstruation based on a state space modeling of Basal Body Temperature time series
Statistics in Medicine, 2017Co-Authors: Keiichi Fukaya, Ai Kawamori, Yutaka Osada, Masumi Kitazawa, Makio IshiguroAbstract: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 that provides a sequentially updated prediction for upcoming menstruation day. We applied this framework to a real data set of women's BBT and menstruation days and compared prediction accuracy of the proposed method with that of previous methods, showing that the proposed method generally provides a better prediction. Because BBT can be obtained with relatively small cost and effort, the proposed method can be useful for women's health management. Potential extensions of this framework as the basis of modeling and predicting events that are associated with the menstrual cycles are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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a self excited threshold autoregressive state space model for menstrual cycles forecasting menstruation and identifying ovarian phases based on Basal Body Temperature
arXiv: Applications, 2017Co-Authors: Ai Kawamori, Keiichi Fukaya, Masumi Kitazawa, Makio IshiguroAbstract:The menstrual cycle is composed of the follicular phase and subsequent luteal phase based on events occurring in the ovary. Basal Body Temperature (BBT) reflects this biphasic aspect of menstrual cycle and tends to be relatively low during the follicular phase. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for BBT switch depend on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of follicular and luteal phases, as well as to estimate the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. An application to a large data set containing 25,622 cycles provided by 3,533 woman subjects further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting wide applicability of the proposed model.
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the forecasting of menstruation based on a state space modeling of Basal Body Temperature time series
arXiv: Applications, 2016Co-Authors: Keiichi Fukaya, Ai Kawamori, Yutaka Osada, Masumi Kitazawa, Makio IshiguroAbstract:Women's Basal Body Temperature (BBT) follows a periodic pattern that is associated with the events in their menstrual cycle. Although daily BBT time series contain potentially useful information for estimating the underlying menstrual phase and for predicting the length of current menstrual cycle, few models have been constructed for BBT time series. Here, we propose a state-space model that includes menstrual phase as a latent state variable to explain fluctuations in BBT and menstrual cycle length. Conditional distributions for the menstrual phase were obtained by using sequential Bayesian filtering techniques. A predictive distribution for the upcoming onset of menstruation was then derived based on the conditional distributions and the model, leading to a novel statistical framework that provided a sequentially updated prediction of the day of onset of menstruation. We applied this framework to a real dataset comprising women's self-reported BBT and days of menstruation, comparing the prediction accuracy of our proposed method with that of conventional calendar calculation. We found that our proposed method provided a better prediction of the day of onset of menstruation. Potential extensions of this framework may provide the basis of modeling and predicting other events that are associated with the menstrual cycle.
Keiichi Fukaya - One of the best experts on this subject based on the ideXlab platform.
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a self excited threshold autoregressive state space model for menstrual cycles forecasting menstruation and identifying within cycle stages based on Basal Body Temperature
Statistics in Medicine, 2019Co-Authors: Ai Kawamori, Keiichi Fukaya, Masumi Kitazawa, Makio IshiguroAbstract:The menstrual cycle is divided into hypothermic and hyperthermic phases based on the periodic shift in the Basal Body Temperature (BBT), reflecting events occurring in the ovary. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for the BBT switch depending on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of the BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of hypothermic and hyperthermic phases, possibly as well as the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. The application of the proposed model to a large data set containing 25 622 cycles provided by 3533 women further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting its wide applicability.
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the forecasting of menstruation based on a state space modeling of Basal Body Temperature time series
Statistics in Medicine, 2017Co-Authors: Keiichi Fukaya, Ai Kawamori, Yutaka Osada, Masumi Kitazawa, Makio IshiguroAbstract: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 that provides a sequentially updated prediction for upcoming menstruation day. We applied this framework to a real data set of women's BBT and menstruation days and compared prediction accuracy of the proposed method with that of previous methods, showing that the proposed method generally provides a better prediction. Because BBT can be obtained with relatively small cost and effort, the proposed method can be useful for women's health management. Potential extensions of this framework as the basis of modeling and predicting events that are associated with the menstrual cycles are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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a self excited threshold autoregressive state space model for menstrual cycles forecasting menstruation and identifying ovarian phases based on Basal Body Temperature
arXiv: Applications, 2017Co-Authors: Ai Kawamori, Keiichi Fukaya, Masumi Kitazawa, Makio IshiguroAbstract:The menstrual cycle is composed of the follicular phase and subsequent luteal phase based on events occurring in the ovary. Basal Body Temperature (BBT) reflects this biphasic aspect of menstrual cycle and tends to be relatively low during the follicular phase. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for BBT switch depend on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of follicular and luteal phases, as well as to estimate the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. An application to a large data set containing 25,622 cycles provided by 3,533 woman subjects further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting wide applicability of the proposed model.
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the forecasting of menstruation based on a state space modeling of Basal Body Temperature time series
arXiv: Applications, 2016Co-Authors: Keiichi Fukaya, Ai Kawamori, Yutaka Osada, Masumi Kitazawa, Makio IshiguroAbstract:Women's Basal Body Temperature (BBT) follows a periodic pattern that is associated with the events in their menstrual cycle. Although daily BBT time series contain potentially useful information for estimating the underlying menstrual phase and for predicting the length of current menstrual cycle, few models have been constructed for BBT time series. Here, we propose a state-space model that includes menstrual phase as a latent state variable to explain fluctuations in BBT and menstrual cycle length. Conditional distributions for the menstrual phase were obtained by using sequential Bayesian filtering techniques. A predictive distribution for the upcoming onset of menstruation was then derived based on the conditional distributions and the model, leading to a novel statistical framework that provided a sequentially updated prediction of the day of onset of menstruation. We applied this framework to a real dataset comprising women's self-reported BBT and days of menstruation, comparing the prediction accuracy of our proposed method with that of conventional calendar calculation. We found that our proposed method provided a better prediction of the day of onset of menstruation. Potential extensions of this framework may provide the basis of modeling and predicting other events that are associated with the menstrual cycle.
Masumi Kitazawa - One of the best experts on this subject based on the ideXlab platform.
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a self excited threshold autoregressive state space model for menstrual cycles forecasting menstruation and identifying within cycle stages based on Basal Body Temperature
Statistics in Medicine, 2019Co-Authors: Ai Kawamori, Keiichi Fukaya, Masumi Kitazawa, Makio IshiguroAbstract:The menstrual cycle is divided into hypothermic and hyperthermic phases based on the periodic shift in the Basal Body Temperature (BBT), reflecting events occurring in the ovary. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for the BBT switch depending on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of the BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of hypothermic and hyperthermic phases, possibly as well as the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. The application of the proposed model to a large data set containing 25 622 cycles provided by 3533 women further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting its wide applicability.
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the forecasting of menstruation based on a state space modeling of Basal Body Temperature time series
Statistics in Medicine, 2017Co-Authors: Keiichi Fukaya, Ai Kawamori, Yutaka Osada, Masumi Kitazawa, Makio IshiguroAbstract: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 that provides a sequentially updated prediction for upcoming menstruation day. We applied this framework to a real data set of women's BBT and menstruation days and compared prediction accuracy of the proposed method with that of previous methods, showing that the proposed method generally provides a better prediction. Because BBT can be obtained with relatively small cost and effort, the proposed method can be useful for women's health management. Potential extensions of this framework as the basis of modeling and predicting events that are associated with the menstrual cycles are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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a self excited threshold autoregressive state space model for menstrual cycles forecasting menstruation and identifying ovarian phases based on Basal Body Temperature
arXiv: Applications, 2017Co-Authors: Ai Kawamori, Keiichi Fukaya, Masumi Kitazawa, Makio IshiguroAbstract:The menstrual cycle is composed of the follicular phase and subsequent luteal phase based on events occurring in the ovary. Basal Body Temperature (BBT) reflects this biphasic aspect of menstrual cycle and tends to be relatively low during the follicular phase. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for BBT switch depend on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of follicular and luteal phases, as well as to estimate the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. An application to a large data set containing 25,622 cycles provided by 3,533 woman subjects further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting wide applicability of the proposed model.
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the forecasting of menstruation based on a state space modeling of Basal Body Temperature time series
arXiv: Applications, 2016Co-Authors: Keiichi Fukaya, Ai Kawamori, Yutaka Osada, Masumi Kitazawa, Makio IshiguroAbstract:Women's Basal Body Temperature (BBT) follows a periodic pattern that is associated with the events in their menstrual cycle. Although daily BBT time series contain potentially useful information for estimating the underlying menstrual phase and for predicting the length of current menstrual cycle, few models have been constructed for BBT time series. Here, we propose a state-space model that includes menstrual phase as a latent state variable to explain fluctuations in BBT and menstrual cycle length. Conditional distributions for the menstrual phase were obtained by using sequential Bayesian filtering techniques. A predictive distribution for the upcoming onset of menstruation was then derived based on the conditional distributions and the model, leading to a novel statistical framework that provided a sequentially updated prediction of the day of onset of menstruation. We applied this framework to a real dataset comprising women's self-reported BBT and days of menstruation, comparing the prediction accuracy of our proposed method with that of conventional calendar calculation. We found that our proposed method provided a better prediction of the day of onset of menstruation. Potential extensions of this framework may provide the basis of modeling and predicting other events that are associated with the menstrual cycle.
Siegfried Pueschel - One of the best experts on this subject based on the ideXlab platform.
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menstrual cycles and Basal Body Temperature curves in women with down syndrome
Obstetrics & Gynecology, 1992Co-Authors: Patricia Scola, Siegfried PueschelAbstract:Menstrual histories were obtained from 51 females with Down syndrome between the ages of 10-27 years. The average age at onset of menstruation of girls in our study was 12 years, 6 months. Seventy-six percent of them had regular menstrual cycles with an average length of menstrual flow of 4 days, and most menstrual cycles lasted 25-30 days. Eight of the women with regular menstrual cycles provided 26 Basal Body Temperature charts. When Basal Body Temperatures were graphed using the smoothed-curve technique, 88.5% had biphasic curves indicative of an ovulatory pattern.
