Int J Med Inform - A methodology to enhance spatial understanding of disease outbreak events reported in news articles.

Tópicos

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Resumo

RPOSE: The emergence and re-emergence of disease outbreaks of international concern in the last several years has raised the importance of health surveillance systems that exploit the open media for their timely and precise detection of events. However, one of the key barriers faced by current event-based health surveillance systems is in identifying fine-grained terms for an outbreak's geographical location. In this article, we present a method to tackle this problem by associating each reported event with the most specific spatial information available in a news report. This would be useful not only for health surveillance systems, but also for other event-centered processing systems.METHODS: To develop an automated spatial attribute annotation system, we first created a gold standard corpus for training a machine learning model. Since the qualitative analysis on data suggested that the event class might have an impact on the spatial attribute annotation, we also developed an event classification system to incorporate event class information into the spatial attribute annotation model. To automatically recognize the spatial attribute of events, several approaches, ranging from a simple heuristic technique to a more sophisticated approach based on a state-of-the-art Conditional Random Fields (CRFs) model were explored. Different feature sets were incorporated into the model and compared.RESULTS: The evaluations were conducted on 100 outbreak news articles. Spatial attribute recognition performance was evaluated based on three metrics; precision, recall and the harmonic mean of precision and recall (F-score). Among three strategies proposed in this article, the CRF model appeared to be the most promising for spatial attribute recognition with a best performance of 85.5% F-score (86.3% precision and 84.7% recall).CONCLUSION: We presented a methodology for associating each event in media outbreak reports with their spatial attribute at the finest level of granularity. Our goal has been to provide a means for enhancing the spatial understanding of outbreak-related events. Evaluation studies showed promising results for automatic spatial attribute annotation. In the future, we plan to explore more features, such as semantic correlation between words, that maybe useful for the spatial attribute annotation task.

Resumo Limpo

rpose emerg reemerg diseas outbreak intern concern last sever year rais import health surveil system exploit open media time precis detect event howev one key barrier face current eventbas health surveil system identifi finegrain term outbreak geograph locat articl present method tackl problem associ report event specif spatial inform avail news report use health surveil system also eventcent process systemsmethod develop autom spatial attribut annot system first creat gold standard corpus train machin learn model sinc qualit analysi data suggest event class might impact spatial attribut annot also develop event classif system incorpor event class inform spatial attribut annot model automat recogn spatial attribut event sever approach rang simpl heurist techniqu sophist approach base stateoftheart condit random field crfs model explor differ featur set incorpor model comparedresult evalu conduct outbreak news articl spatial attribut recognit perform evalu base three metric precis recal harmon mean precis recal fscore among three strategi propos articl crf model appear promis spatial attribut recognit best perform fscore precis recallconclus present methodolog associ event media outbreak report spatial attribut finest level granular goal provid mean enhanc spatial understand outbreakrel event evalu studi show promis result automat spatial attribut annot futur plan explor featur semant correl word mayb use spatial attribut annot task

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