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BioCaster 2.0: Online text analysis for early alerting of disease outbreaks

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If you have a question about this talk, please contact Thomas Lippincott .

Accurate and timely detection of infectious disease outbreak events of international concern is necessary to help support risk assessment and ultimately to save lives and livelihoods. In this talk I will introduce high throughput biomedical fact extraction from the ‘grey’ literature – the large body of non- peer reviewed texts on the Web and consider how we might meet the challenge of deciding which events are ‘unusual’. Modeling ‘norms’ and detecting their violations raises several key issues which will be touched on in the talk: (1) how to bridge the ambiguity gap between laymen’s terms and extant biomedical ontological resources, (2) how to interpret evidence across documents, (3) how to achieve real time processing of thousands of documents per hour, (4) how we might provide a realistic benchmark standard for alerting.

This talk is part of the NLIP Seminar Series series.

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Meeting Rooms 1&2, MRC Epidemiology Unit, Level 3, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ Education Society Cambridge (ESC) Average on weekdays: 9201.78791615 kWh

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