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In a typical Event-Based Surveillance setting, a stream of web documents iscontinuously monitored for disease reporting. A structured representation ofthe disease reporting events is extracted from the raw text, and the events arethen aggregated to produce signals, which are intended to represent earlywarnings against potential public health threats.  To public health officials, these warnings represent an overwhelming list of"one-size-fits-all" information for risk assessment. To reduce this overload,two techniques are proposed. First, filtering signals according to the user'spreferences (e.g., location, disease, symptoms, etc.) helps reduce theundesired noise. Second, re-ranking the filtered signals, according to anindividual's feedback and annotation, allows a user-specific, prioritizedranking of the most relevant warnings.  We introduce an approach that takes into account this two-step process of: 1)filtering and 2) re-ranking the results of reporting signals. For this,Collaborative Filtering and Personalization are common techniques used tosupport users in dealing with the large amount of information that they face.

Personalized Event-Based Surveillance and Alerting Support for the Assessment of Risk