NLM Informatics and Data Science Lecture Series: Advancing Women's Health through Data Science and Personal Health Informatics

NLM Informatics and Data Science Lecture Series Endometriosis is a chronic, inflammatory, and estrogen-dependent condition with a high burden on quality of life, estimated to affect 6-10% of women of reproductive age worldwide. Despite its high prevalence, it is an enigmatic condition: there is currently no cure and no known biomarker or non-invasive diagnostic test for this multifactorial disease. In this talk, Dr. Elhadad will report on ongoing research on two inter-related questions: how to characterize and discover the different ways in which endometriosis presents in individuals, essentially phenotyping the disease, and how to support individuals with self-discovery and management about the disease considering its heterogeneous presentations. She will show the current characterization of endometriosis from clinical data sources and discuss its current limitations, specifically the disconnect with the day-to-day patient experience of endometriosis. She will present the design and development of a personal health informatics solution (a research app called Phendo) and the analysis of the data contributed by Phendo participants towards phenotyping endometriosis. Finally, She will discuss how these data can be leveraged further to support individuals in learning about and self-managing their condition, as well as facilitating shared decision making with their providers. No é mie Elhadad is Associate Professor and co-interim Chair at the Department of Biomedical Informatics ...
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