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

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

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Air date:	Wednesday, June 12, 2019, 2:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 239 (138 Live, 101 On-demand)
Category:	Special
Runtime:	00:56:13
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	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. Noémie Elhadad is Associate Professor and co-interim Chair at the Department of Biomedical Informatics at Columbia University, affiliated with the Columbia Computer Science Department and Data Science Institute. She received her PhD in Computer Science from Columbia University. Her research is at the intersection of machine learning, technology, and medicine. She investigates ways in which observational clinical data (e.g., electronic health records) and patient-generated data (e.g., online health community discussions, mobile health data) can enhance access to relevant information for patients, clinicians, and health researchers alike and can impact care and health of patients. Dr. Elhadad is a current member of NLM's Biomedical Informatics, Library and Data Sciences Review Committee.

NLM Title:	Advancing women's health through data science and personal health informatics / Noémie Elhadad.
Author:	Elhadad, Noémie. National Library of Medicine (U.S.),
Publisher:
Abstract:	(CIT): 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. Noemie Elhadad is Associate Professor and co-interim Chair at the Department of Biomedical Informatics at Columbia University, affiliated with the Columbia Computer Science Department and Data Science Institute. She received her PhD in Computer Science from Columbia University. Her research is at the intersection of machine learning, technology, and medicine. She investigates ways in which observational clinical data (e.g., electronic health records) and patient-generated data (e.g., online health community discussions, mobile health data) can enhance access to relevant information for patients, clinicians, and health researchers alike and can impact care and health of patients. Dr. Elhadad is a current member of NLM's Biomedical Informatics, Library and Data Sciences Review Committee.
Subjects:	Data Science Endometriosis--genetics Health Records, Personal Medical Informatics Applications
Publication Types:	Lecture Webcast

NLM Classification:	WP 390
NLM ID:	101750707
CIT Live ID:	33148
Permanent link:	https://videocast.nih.gov/watch=33148

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