Machine learning to refine decision making within a syndromic surveillance service

Machine learning to refine decision making within a syndromic surveillance service

Abstract Background Worldwide, syndromic surveillance is increasingly used for improved and timely situational awareness and early identification of public health threats. Syndromic data streams are fed into detection algorithms, which produce statistical alarms highlighting potential activity of pu...

Full description

Bibliographic Details
Main Authors: I. R. Lake, F. J. Colón-González, G. C. Barker, R. A. Morbey, G. E. Smith, A. J. Elliot
Format: Article
Language:English
Published: BMC 2019-05-01
Series:BMC Public Health
Subjects:
Syndromic surveillance
Public health
Decision making
Bayes’ theorem
Machine learning
Artificial intelligence
Online Access:http://link.springer.com/article/10.1186/s12889-019-6916-9

Internet

http://link.springer.com/article/10.1186/s12889-019-6916-9

Similar Items