This paper is in the following e-collection/theme issue:

Presentations, Abstracts, or Demonstrations (740) Shared Electronic Health Records and Health Information Systems (68)

Published on in Vol 5, No 1 (2013):

Open Source Health Intelligence (OSHINT) for Foodborne Illness Event Characterization

Open Source Health Intelligence (OSHINT) for Foodborne Illness Event Characterization

Open Source Health Intelligence (OSHINT) for Foodborne Illness Event Characterization

Authors of this article:

Catherine Ordun1 ;   Jane W. Blake1 ;   Nathanael Rosidi1 ;   Vahan Grigoryan1 ;   Christopher Reffett1 ;   Sadia Aslam1 ;   Anastasia Gentilcore1 ;   Marek Cyran1 ;   Matthew Shelton1 ;   Juergen Klenk1
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  2. Hemphill L, Schöpke-Gonzalez A, Panda A. Comparative sensitivity of social media data and their acceptable use in research. Scientific Data 2022;9(1) View
  3. Tao D, Yang P, Feng H. Utilization of text mining as a big data analysis tool for food science and nutrition. Comprehensive Reviews in Food Science and Food Safety 2020;19(2):875 View
  4. Nsoesie E, Kluberg S, Brownstein J. Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports. Preventive Medicine 2014;67:264 View
  5. Tao D, Zhang D, Hu R, Rundensteiner E, Feng H. Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media. Scientific Reports 2021;11(1) View