Post-marketing management and decision-making about vaccines builds on the early detection of safety concerns and changes in public sentiment, the accurate access to established evidence, and the ability to promptly quantify effects and verify hypotheses about the vaccine benefits and risks. A variety of resources provide relevant information but they use different representations, which makes rapid evidence generation and extraction challenging. This thesis presents automatic methods for interpreting heterogeneously represented vaccine information. Part I evaluates social media messages for monitoring vaccine adverse events and public sentiment in social media messages, using automatic methods for information recognition. Parts II and III develop and evaluate automatic methods and resources for the recognition, representation, and reasoning about established vaccine-related information in scientific literature and extracting information from medical health record databases. Additionally, two user applications, CodeMapper and VaccO, are introduced to accellerate the implementation of collaborative observational studies about vaccines.

Additional Metadata
Keywords Vaccines, Medical coding systems, Ontologies, Text mining, Information extraction
Promotor M.C.J.M. Sturkenboom (Miriam) , J.A. Kors (Jan)
Publisher Erasmus University Rotterdam
Persistent URL hdl.handle.net/1765/111218
Note For copyright reasons there is a partial embargo for this dissertation
Citation
Becker, B.F.H. (2019, January 8). Vaccine semantics : Automatic methods for recognizing, representing, and reasoning about vaccine-related information. Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/111218