Iñigo Urteaga

ObjectiveThis study examined the use of machine learning (ML) and domain-specific enrichment in patient-generated health data, in the form of free-text meal logs, to classify meals on alignment with different nutritional goals.Materials and…

Intelligent systems for self-management can help patients and improve quality of life. However, designing AI-based systems is challenging because designers need to account not only for user needs, but also for capabilities and practical con…

Abstract We characterize short-term and long-term user engagement patterns in a self-tracking, mobile health app. We introduce and define engagement metrics to capture the quantity, duration, and density of participant engagement according…

ObjectiveThe 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 …

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 provi…

Abstract 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 o…

We investigate the use of self-tracking data and unsupervised mixed-membership models to phenotype endometriosis. Endometriosis is a systemic, chronic condition of women in reproductive age and, at the same time, a highly enigmatic conditio…