Title :
Self-supervised detection of disease reporting events in outbreak reports
Author :
Stewart, Avaré ; Diaz-Aviles, Ernesto ; Nanopoulos, Alexandros
Author_Institution :
L3S Res. Center, Hannover, Germany
Abstract :
State-of-the-art supervised approaches for automatically detecting disease reporting events are typically constructed using manual training examples. Such systems suffer from high initial, and sustainability costs. This paper addresses the aforementioned problem, laying the groundwork for a new approach to disease reporting classification for Epidemic Intelligence. Instead of building a classifier strictly from manually labeled data, we exploit outbreak reports, to build a self-supervised classifier, one that labels its own training examples. We measure the performance of our self-supervised classifier and find that it achieves an accuracy of 88%, comparable with existing, state-of-the-art systems. The implications for this work is that by using a self-supervised learner, Epidemic Intelligence systems can build and deploy reliable classifiers; bringing them one step closer to detecting infectious disease threats from on-line informal sources, more quickly.
Keywords :
artificial intelligence; diseases; epidemics; medical computing; pattern classification; disease reporting classification; disease reporting events; epidemic intelligence systems; infectious disease threat detection; manual training examples; online informal sources; outbreak reports; self-supervised classifier; self-supervised detection; sustainability costs; Diseases; Humans; Labeling; Manuals; Semantics; Support vector machines; Training;
Conference_Titel :
Information Reuse and Integration (IRI), 2011 IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4577-0964-7
Electronic_ISBN :
978-1-4577-0965-4
DOI :
10.1109/IRI.2011.6009584