Elhadad, N., Urteaga, I., Lipsky-Gorman, S., McKillop, M. User Engagement Metrics and Patterns in Phendo, an Endometriosis Research Mobile App. (2022).

Ensari, I., Horan, E., Elhadad, N., Bakken, S. Evaluation of a disease-specific mHealth-based exercise self-tracking measure. medRxiv. (2022).

Ensari, I., Lipsky-Gorman, S., Horan, E., Bakken, S., Elhadad, N. Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation. BMJ Open. 12 (2022).

Pichon, A., Jackman, K., Winkler, I., Bobel, C., Elhadad, N. The messiness of the menstruator: assessing personas and functionalities of menstrual tracking apps. Journal of the American Medical Informatics Association. 29, 385-399 (2021)

Pichon, A., Schiffer, K., Horan, E., Massey, B., Bakken, S., Mamykina, L., Elhadad, N. Divided We Stand: The Collaborative Work of Patients and Providers in an Enigmatic Chronic Disease. Proc. of the ACM on Human-Computer Interaction. 4, 1-24 (2021).

Urteaga, I., McKillop, M., Elhadad N. Learning endometriosis phenotypes from patient-generated data. NPJ Digital Medicine. 3, 88 (2020).

Li, K., Urteaga, I., Wiggins, C.H., Druet, A., Shea, A., Vitzthum, V, Elhadad, N. Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. NPJ Digital Medicine. 3, 1-13 (2020).

Graham, E., Elhadad, N., Albers, D. 2020. Reduced model for female endocrine dynamics: Validation and functional variations. arXiv preprint arXiv:2006.05034. [arxiv]

Pratap, A., Neto, E.C., Snyder, P., Stepnowsky, C., Elhadad, N., Grant, D., Mohebbi, M., Mooney, S., Suver, C., Wilbanks, J., Mangravite, L., Heagerty, P., Arean, P., Omberg, L. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digital Medicine. 3, 1-10 (2020).

Houghton, L., Elhadad, N. 2020. Practice Note: ‘If Only All Women Menstruated Exactly Two Weeks Ago’: Interdisciplinary Challenges and Experiences of Capturing Hormonal Variation Across the Menstrual Cycle. The Palgrave Handbook of Critical Menstruation Studies: 725-732. [html]

Urteaga, I., Bertin, T., Hardy, T., Albers, D., Elhadad, N. 2019. Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics. Machine Learning for Healthcare (MLHC’19). [arxiv]

Urteaga, I., McKillop, M., Gorman, S., Elhadad N. Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data. in 2018 Machine Learning for Healthcare (2018).

McKillop, M., Mamykina, L., Elhadad, N. Designing in the Dark: Eliciting Self-Tracking Dimensions for Understanding Enigmatic Disease. ACM CHI Conference on Human Factors in Computing Systems (2018).

Urteaga, I., Albers, D., Wheeler, M., Druet, A., Raffauf, H., Elhadad, N. 2017. Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes. Machine Learning for Health Workshop at NIPS (ML4H’17). [arxiv]

Zhang S., Grave, E., Sklar, E., Elhadad, N. 2017. Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks. Journal of Biomedical Informatics 69: 1-9. [html]

Zhang, S., Qiu, L., Chen, F., Zhang, W., Yu, Y., Elhadad, N. 2017. We make choices we think are going to save us: Debate and stance identification for online breast cancer CAM discussions. Proceedings of the 26th International Conference on World Wide Web Companion: 1073-1081. [html]

Zhang, S., O’Carroll Bantum, E., Owen, J., Bakken, S., Elhadad, N. 2017. Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact. Journal of the American Medical Informatics Association 24 (2): 451-459. [html]

McKillop, M., Voigt, N., Schnall, R., Elhadad, N. Exploring self-tracking as a participatory research activity among women with endometriosis. Journal of Participatory Medicine. 8 (2016).