Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data
This work addresses the challenge of monitoring endometriosis progression for women with the condition, offering a novel data-driven approach to subtype identification, though it is incremental in method adaptation.
The researchers tackled the problem of phenotyping endometriosis by using self-tracking data from over 2,800 women and an extended mixed-membership model, identifying robust and clinically meaningful subtypes that validate known aspects and suggest new findings.
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.