NIH VideoCast - AI in the Age of COVID-19: Computational Tools for the Classification, Prediction, and Characterization of a Pandemic

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AI in the Age of COVID-19: Computational Tools for the Classification, Prediction, and Characterization of a Pandemic

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Air date:	Wednesday, June 24, 2020, 1:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 986 (623 Live, 363 On-demand)
Category:	Ada Lovelace
Runtime:	01:02:52
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	NLM Ada Lovelace Computational Health Lecture Series Traditional methods of epidemic modeling continue to be used fruitfully for characterizing outbreaks and predicting the spread of disease in populations. However, these methods, typically relying on what are known as “compartment models”, require assumptions that are not necessarily sensitive to the ever-changing environmental, behavioral, temporospatial, and social phenomena that influence disease spread. However, compartment models can be enriched by the judicious use of robust methods drawn from the field of artificial intelligence that allow us to model more accurately and more quickly the population and disease dynamics that are central to developing policies for prevention, detection, and treatment. We will explore these approaches, including some that are currently in use as well as a proposal for novel, next-generation machine learning tools for epidemiologic investigation. John H. Holmes, PhD, is Professor of Medical Informatics in Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is the Associate Director of the Penn Institute for Biomedical Informatics and is Past-Chair of the Graduate Group in Epidemiology and Biostatistics. Dr. Holmes has been recognized nationally and internationally for his work on developing and applying new artificial intelligence approaches to mining epidemiologic surveillance data. Dr. Holmes’ research interests are focused on the intersection of medical informatics and clinical research, specifically evolutionary computation and machine learning approaches to knowledge discovery in clinical databases, deep electronic phenotyping, interoperable information systems infrastructures for epidemiologic surveillance, and their application to a broad array of clinical domains, including cardiology and pulmonary medicine. He has served as the co-lead of the Governance Core for the SPAN project, a scalable distributed research network, and participates in the FDA Sentinel Initiative. Dr. Holmes is an elected Fellow of the American College of Medical Informatics (ACMI), the American College of Epidemiology (ACE), and the International Academy of Health Sciences Informatics Medical Informatics (ACMI), the American College of Epidemiology (ACE), and the International Academy of Health Sciences Informatics (IAHSI).

NLM Title:	AI in the age of COVID-19 : computational tools for the classification, prediction, and characterization of a pandemic / John H. Holmes.
Author:	Holmes, John. National Institutes of Health (U.S.),
Publisher:
Abstract:	(CIT): NLM Ada Lovelace Computational Health Lecture Series. Traditional methods of epidemic modeling continue to be used fruitfully for characterizing outbreaks and predicting the spread of disease in populations. However, these methods, typically relying on what are known as "compartment models", require assumptions that are not necessarily sensitive to the ever-changing environmental, behavioral, temporospatial, and social phenomena that influence disease spread. However, compartment models can be enriched by the judicious use of robust methods drawn from the field of artificial intelligence that allow us to model more accurately and more quickly the population and disease dynamics that are central to developing policies for prevention, detection, and treatment. We will explore these approaches, including some that are currently in use as well as a proposal for novel, next-generation machine learning tools for epidemiologic investigation. John H. Holmes, PhD, is Professor of Medical Informatics in Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is the Associate Director of the Penn Institute for Biomedical Informatics and is Past-Chair of the Graduate Group in Epidemiology and Biostatistics. Dr. Holmes has been recognized nationally and internationally for his work on developing and applying new artificial intelligence approaches to mining epidemiologic surveillance data. Dr. Holmes' research interests are focused on the intersection of medical informatics and clinical research, specifically evolutionary computation and machine learning approaches to knowledge discovery in clinical databases, deep electronic phenotyping, interoperable information systems infrastructures for epidemiologic surveillance, and their application to a broad array of clinical domains, including cardiology and pulmonary medicine. He has served as the co-lead of the Governance Core for the SPAN project, a scalable distributed research network, and participates in the FDA Sentinel Initiative. Dr. Holmes is an elected Fellow of the American College of Medical Informatics (ACMI), the American College of Epidemiology (ACE), and the International Academy of Health Sciences Informatics Medical Informatics (ACMI), the American College of Epidemiology (ACE), and the International Academy of Health Sciences Informatics (IAHSI).
Subjects:	Artificial Intelligence Coronavirus Infections--epidemiology Disease Transmission, Infectious Models, Statistical Pandemics Pneumonia, Viral--epidemiology
Publication Types:	Lecture Webcast

NLM Classification:	WC 505
NLM ID:	101769639
CIT Live ID:	37909
Permanent link:	https://videocast.nih.gov/watch=37909

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