  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 condition with no known biomarkers to monitor its
progression and no established staging. We leverage data collected through a
self-tracking app in an observational research study of over 2,800 women with
endometriosis tracking their condition over a year and a half (456,900
observations overall). We extend a classical mixed-membership model to
accommodate the idiosyncrasies of the data at hand (i.e., the multimodality of
the tracked variables). Our experiments show that our approach identifies
potential subtypes that are robust in terms of biases of self-tracked data
(e.g., wide variations in tracking frequency amongst participants), as well as
to variations in hyperparameters of the model. Jointly modeling a wide range of
observations about participants (symptoms, quality of life, treatments) yields
clinically meaningful subtypes that both validate what is already known about
endometriosis and suggest new findings.
