NIH VideoCast - Knowledge Discovery in Clinical and Biomedical Data: Case Studies in Pediatrics and Mental Health

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Knowledge Discovery in Clinical and Biomedical Data: Case Studies in Pediatrics and Mental Health

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Air date:	Wednesday, November 14, 2018, 2:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 191 (112 Live, 79 On-demand)
Category:	Special
Runtime:	01:08:56
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	With the widespread adoption of electronic health records and increasing discoveries reported in biomedical literature, computational approaches are needed for further knowledge discovery and hypothesis generation. Challenges include the capture of key information within text and standardization issues, requiring use of natural language processing and data integration techniques. Clinical data mining and biomedical literature mining have been used in a range of contexts to discover disease knowledge such as comorbidities and patterns related to social, behavioral, and familial (SBF) factors. In this lecture, a series of case studies will be presented on representing, extracting, integrating, mining, and visualizing SBF factors and comorbidities for pediatric and mental health conditions. Collectively, these studies demonstrate use of systematic processes and development of open-source tools for transforming clinical and biomedical data into knowledge. Elizabeth Chen is the Founding Associate Director of the Brown Center for Biomedical Informatics (BCBI), Associate Professor of Medical Science, and Associate Professor of Health Services, Policy & Practice at Brown University. She received a BS in Computer Science from Tufts University and PhD in Biomedical Informatics from Columbia University. Within BCBI, Dr. Chen leads the Clinical Informatics Innovation and Implementation (CI3) Laboratory that is focused on leveraging EHR technology and data to improve healthcare delivery and biomedical discovery. Her research interests include clinical documentation, clinical decision support, health information needs, standards and interoperability, natural language processing, and data mining. Dr. Chen is an elected fellow of the American College of Medical Informatics and is currently a member of NLM’s Biomedical Informatics, Library and Data Sciences Review Committee.

NLM Title:	Knowledge discovery in clinical and biomedical data : case studies in pediatrics and mental health / Elizabeth Chen.
Author:	Chen, Elizabeth. National Institutes of Health (U.S.),
Publisher:
Abstract:	(CIT): With the widespread adoption of electronic health records and increasing discoveries reported in biomedical literature, computational approaches are needed for further knowledge discovery and hypothesis generation. Challenges include the capture of key information within text and standardization issues, requiring use of natural language processing and data integration techniques. Clinical data mining and biomedical literature mining have been used in a range of contexts to discover disease knowledge such as comorbidities and patterns related to social, behavioral, and familial (SBF) factors. In this lecture, a series of case studies will be presented on representing, extracting, integrating, mining, and visualizing SBF factors and comorbidities for pediatric and mental health conditions. Collectively, these studies demonstrate use of systematic processes and development of open-source tools for transforming clinical and biomedical data into knowledge. Elizabeth Chen is the Founding Associate Director of the Brown Center for Biomedical Informatics (BCBI), Associate Professor of Medical Science, and Associate Professor of Health Services, Policy & Practice at Brown University. She received a BS in Computer Science from Tufts University and PhD in Biomedical Informatics from Columbia University. Within BCBI, Dr. Chen leads the Clinical Informatics Innovation and Implementation (CI3) Laboratory that is focused on leveraging EHR technology and data to improve healthcare delivery and biomedical discovery. Her research interests include clinical documentation, clinical decision support, health information needs, standards and interoperability, natural language processing, and data mining. Dr. Chen is an elected fellow of the American College of Medical Informatics and is currently a member of NLM's Biomedical Informatics, Library and Data Sciences Review Committee.
Subjects:	Adult Asthma Child Data Interpretation, Statistical Data Mining Mental Disorders
Publication Types:	Lecture Webcast

NLM Classification:	WF 553
NLM ID:	101738456
CIT Live ID:	28781
Permanent link:	https://videocast.nih.gov/watch=28781

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