A Bayesian system to detect and characterize overlapping outbreaks

Aronis JM, Millett NE, Wagner MW, Tsui F, Ye Y, Ferraro JP, Haug PJ, Gesteland PH, Cooper GF. A Bayesian system to detect and characterize overlapping outbreaks. Journal of Biomedical Informatics 2017 Sep;73:171-181. doi: 10.1016/j.jbi.2017.08.003. Epub 2017 Aug 7. PMID: 28797710 PMC5604259

We describe an automated system that can detect multiple outbreaks of infectious diseases from emergency department reports. A case detection system1 obtains data from electronic medical records, extracts features using natural language processing, then infers a probability distribution over the diseases each patient may have. Then, a multiple outbreak detection system (MODS) searches for models of multiple outbreaks to explain the data. MODS detects outbreaks of influenza and non-influenza influenza-like illnesses (NI-ILI).
 

Publication Year: 
2017
Faculty Author: 
Publication Credits: 
Aronis JM, Millett NE, Wagner MW, Tsui F, Ye Y, Ferraro JP, Haug PJ, Gesteland PH, Cooper GF
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