@article{Hawkins_Tuli_Kluberg_Harris_Brownstein_Nsoesie_2016, title={A Digital Platform for Local Foodborne Illness and Outbreak Surveillance}, volume={8}, url={https://ojphi.org/ojs/index.php/ojphi/article/view/6474}, DOI={10.5210/ojphi.v8i1.6474}, abstractNote={<p class="p1">Foodborne illness affects 1 in 4 Americans, annually. However, only a fraction of affected individuals seek medical attention. In this presentation, we will discuss our collaboration with local public health departments to develop a foodborne disease surveillance platform to supplement ongoing surveillance efforts. The platform currently uses digital data from Twitter and Yelp. We developed a machine learning classifier to differentiate between relevant and irrelevant data. The classifier had an accuracy and precision of 85% and 82%, respectively based on an evaluation using 6084 tweets. These performance results are promising, especially given the similarities between the data classes.</p>}, number={1}, journal={Online Journal of Public Health Informatics}, author={Hawkins, Jared B. and Tuli, Gaurav and Kluberg, Sheryl and Harris, Jenine and Brownstein, John S. and Nsoesie, Elaine}, year={2016}, month={Mar.} }