A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking.

OBJECTIVE: The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models. MATERIALS AND METHODS: We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information. RESULTS: Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities. DISCUSSION: Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure. CONCLUSIONS: Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.

A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction.

We explore how to quantify uncertainty when designing predictive models for healthcare to provide well-calibrated results. Uncertainty quantification and calibration are critical in medicine, as one must not only accommodate the variability of the underlying physiology, but adjust to the uncertain data collection and reporting process. This occurs not only on the context of electronic health records (i.e., the clinical documentation process), but on mobile health as well (i.e., user specific self-tracking patterns must be accounted for). In this work, we show that accurate uncertainty estimation is directly relevant to an important health application: the prediction of menstrual cycle length, based on self-tracked information. We take advantage of a flexible generative model that accommodates under-dispersed distributions via two degrees of freedom to fit the mean and variance of the observed cycle lengths. From a machine learning perspective, our work showcases how flexible generative models can not only provide state-of-the art predictive accuracy, but enable well-calibrated predictions. From a healthcare perspective, we demonstrate that with flexible generative models, not only can we accommodate the idiosyncrasies of mobile health data, but we can also adjust the predictive uncertainty to per-user cycle length patterns. We evaluate the proposed model in real-world cycle length data collected by one of the most popular menstrual trackers worldwide, and demonstrate how the proposed generative model provides accurate and well-calibrated cycle length predictions. Providing meaningful, less uncertain cycle length predictions is beneficial for menstrual health researchers, mobile health users and developers, as it may help design more usable mobile health solutions.

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink GmbH of over 378,000 users and 4.9 million natural cycles, we show that self-reported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and self-reported qualitative symptoms. A concern for self-tracked data is that they reflect not only physiological behaviors, but also the engagement dynamics of app users. To mitigate such potential artifacts, we develop a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We uncover that women located at different ends of the menstrual variability spectrum, based on the consistency of their cycle length statistics, exhibit statistically significant differences in their cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms, or as potential health indicators for conditions like endometriosis. Our findings showcase the potential of longitudinal, high-resolution self-tracked data to improve understanding of menstruation and women's health as a whole.

Learning endometriosis phenotypes from patient-generated data.

Endometriosis is a systemic and chronic condition in women of childbearing age, yet a highly enigmatic disease with unresolved questions: there are no known biomarkers, nor established clinical stages. We here investigate the use of patient-generated health data and data-driven phenotyping to characterize endometriosis patient subtypes, based on their reported signs and symptoms. We aim at unsupervised learning of endometriosis phenotypes using self-tracking data from personal smartphones. We leverage data from an observational research study of over 4000 women with endometriosis that track their condition over more than 2 years. We extend a classical mixed-membership model to accommodate the idiosyncrasies of the data at hand, i.e., the multimodality and uncertainty of the self-tracked variables. The proposed method, by jointly modeling a wide range of observations (i.e., participant symptoms, quality of life, treatments), identifies clinically relevant endometriosis subtypes. Experiments show that our method is robust to different hyperparameter choices and the biases of self-tracking data (e.g., the wide variations in tracking frequency among participants). With this work, we show the promise of unsupervised learning of endometriosis subtypes from self-tracked data, as learned phenotypes align well with what is already known about the disease, but also suggest new clinically actionable findings. More generally, we argue that a continued research effort on unsupervised phenotyping methods with patient-generated health data via new mobile and digital technologies will have significant impact on the study of enigmatic diseases in particular, and health in general.

On integrating groundwater transport models with wireless sensor networks.

The emerging technology of wireless sensor networks (WSNs) is an integrated, distributed, wireless network of sensing devices. It has the potential to monitor dynamic hydrological and environmental processes more effectively than traditional monitoring and data acquisition techniques by providing environmental information at greater spatial and temporal resolutions. Furthermore, due to continuing high-performance computing development, these data may be introduced into increasingly robust and complex numerical models; for instance, the parameters of subsurface transport simulators may be automatically updated. Early field deployments and laboratory experiments conducted using in situ sensor technology and WSNs indicated significant fundamental issues concerning sensor and network hardware reliability-suggesting that investigations should first be conducted in controlled environments before field deployment. A first step in this validation process involves evaluating the predictive capability of a computational advection-dispersion transport model when incorporating concentration data from a WSN simulation. Data quality is a major concern, especially when sensor readings are automatically fed into data assimilation procedures. The appropriate employment of an independent WSN fault detection service can ensure that erroneous data (e.g., missing or anomalous values) do not mislead the model. Parameter estimation regularization techniques may then deal with remaining data noise. The primary purpose of this study is to determine the suitability of WSNs (and other in situ data delivery technologies) for use in contaminant transport modeling applications by conducting research in a realistic simulative environment.