Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Nov 1, 2019

Abstract

OBJECTIVE: We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports.

Authors

Joshua Feldman

Harvard University, School of Engineering and Applied Sciences, Cambridge, Massachusetts, USA.
Andrea Thomas-Bachli

Li Ka Shing Knowledge Institute, St. Michaels Hospital, Toronto, Ontario, Canada.
Jack Forsyth

Li Ka Shing Knowledge Institute, St. Michaels Hospital, Toronto, Ontario, Canada.
Zaki Hasnain Patel

Li Ka Shing Knowledge Institute, St. Michaels Hospital, Toronto, Ontario, Canada.
Kamran Khan

Li Ka Shing Knowledge Institute, St. Michaels Hospital, Toronto, Ontario, Canada.

Keywords

Communicable Diseases Databases, Factual Disease Outbreaks Global Health Humans Information Storage and Retrieval Machine Learning Natural Language Processing Population Surveillance User-Computer Interface

External Resources

View on PubMed Access via DOI PubMed (31361300)

Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise.

Abstract

Authors

Keywords

External Resources

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