Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. - Kathy Li; Iñigo Urteaga; Chris H. Wiggins 001; Anna Druet; Amanda Shea; Virginia J. Vitzthum; Noémie Elhadad (npj Digit. Medicine, 2020)

A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction. - Iñigo Urteaga; Kathy Li; Amanda Shea; Virginia J. Vitzthum; Chris H. Wiggins 001; Noemie Elhadad (MLHC, 2021)

A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data. - Kathy Li; Iñigo Urteaga; Amanda Shea; Virginia J. Vitzthum; Chris H. Wiggins 001; Noémie Elhadad (CoRR, 2021)

A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking. - Kathy Li; Iñigo Urteaga; Amanda Shea; Virginia J. Vitzthum; Chris H. Wiggins 001; Noémie Elhadad (J. Am. Medical Informatics Assoc., 2021)