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Phenotyping Endometriosis Through Mixed Membership Models Of Self-Tracking DataAbstract: Despite the impressive past and recent advances in medical sciences, there are still a host of chronic conditions which are not well understood and lack even consensus description of their signs and symptoms. Without such consensus, research for precise treatments and ultimately a cure is at a halt. Phenotyping these conditions, that is, systematically characterizing the signs, symptoms and other aspects of these conditions, is thus particularly needed. Computational phenotyping can help identify cohorts of patients at scale and identify potential sub-groups, thus generating new hypotheses for these mysterious conditions. While traditional phenotyping algorithms rely on clinical documentation and expert knowledge, phenotyping for enigmatic conditions might benefit from patient expertise as well. In this talk I will focus on one such enigmatic condition, endometriosis, a chronic condition estimated to affect 10% of women in reproductive age. I will describe approaches needed to phenotype the condition: eliciting dimensions of disease, engaging patients in self-tracking their condition, and discovering phenotypes and sub-phenotypes of endometriosis based on patients' accounts of the disease. Bio: Noemie Elhadad is an Associate Professor in Biomedical Informatics, affiliated with Computer Science and the Data Science Institute at Columbia University. Her research is at the intersection of computation, technology, and medicine with a focus on machine learning for healthcare and natural language processing of clinical and health texts. Her work is funded by the National Science Foundation, the National Library of Medicine, the National Cancer Institute, and the National Institute for General Medical Sciences. |