A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking
Autor: | Kathy Li, Virginia J. Vitzthum, Noémie Elhadad, Amanda Shea, Iñigo Urteaga, Chris H. Wiggins |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Change over time
self-management AcademicSubjects/SCI01060 BitTorrent tracker Computer science Reliability (computer networking) Health Informatics Machine learning computer.software_genre Research and Applications Convolutional neural network Humans AcademicSubjects/MED00580 Menstrual Cycle Self-management business.industry Self tracking Reproducibility of Results Data structure Mobile Applications Telemedicine Featured Menstruation Recurrent neural network machine learning Female Artificial intelligence AcademicSubjects/SCI01530 business computer |
Zdroj: | Journal of the American Medical Informatics Association : JAMIA |
ISSN: | 1527-974X 1067-5027 |
Popis: | 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. |
Databáze: | OpenAIRE |
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