Machine learning techniques and research framework in foodborne disease surveillance system.

Based on the verified application in China's National Foodborne Disease Outbreak Surveillance System, machine learning techniques have been indicated to have very positive effects. We summarize current positive attempts on improving case reporting, helping diagnosing and outbreak prediction. Based on the attempts, we propose a general framework to facilitate future improvements for a more intelligent foodborne disease surveillance system, which can greatly help human health. • Summarize machine learning (ML) techniques that have positive effect in case reporting, diagnosis and outbreak prediction.. • Generalize current ML effects to a more general scope, including individual, population and spatial temporal requirements. • Propose a general framework that can guide future research on using ML techniques to improve existing surveillance system. [ABSTRACT FROM AUTHOR]