• Corpus ID: 53213190

Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data

@article{Urteaga2018PhenotypingET,
  title={Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data},
  author={I{\~n}igo Urteaga and Mollie M. McKillop and Sharon Lipsky Gorman and No{\'e}mie Elhadad},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.03431}
}
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… 
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