%0 Conference Paper %1 5936610 %A Stewart, A. %A Herder, E. %A Smith, M. %A Nejdl, W. %B Digital Ecosystems and Technologies Conference (DEST), 2011 Proceedings of the 5th IEEE International Conference on %D 2011 %K 2011 events health m-eco myown public %P 127 -132 %R 10.1109/DEST.2011.5936610 %T A user study on public health events detected within the medical ecosystem %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=5936610 %X The great influx of Medical-Web data makes the task of computer-assisted gathering and interpretation of Social Media-based Epidemic Intelligence (SM-EI) a very challenging one. State-of-the-art approaches usually use supervised machine learning algorithms to gather data from a variety of sources in this medical ecosystem, mining this data for specific event patterns and information discovery. Supervised approaches not only limit the type of detectable events, but also requires learning examples be given to the machine learning algorithm in advance. On the other hand, the more generic and flexible unsupervised machine learning methods currently produce such complex results, that the domain experts are not capable of assessing the results in a natural and efficient manner. In this paper, we present a novel framework with which SM-EI field practitioners can interact with medical ecosystem data, and assess the results of such complex unsupervised SM-EI algorithms. The assessment framework and the unsupervised epidemic event detection algorithm have been fully implemented and a quantitative study is presented to show the validity of the new approach to SM-EI.

@inproceedings{5936610, abstract = {The great influx of Medical-Web data makes the task of computer-assisted gathering and interpretation of Social Media-based Epidemic Intelligence (SM-EI) a very challenging one. State-of-the-art approaches usually use supervised machine learning algorithms to gather data from a variety of sources in this medical ecosystem, mining this data for specific event patterns and information discovery. Supervised approaches not only limit the type of detectable events, but also requires learning examples be given to the machine learning algorithm in advance. On the other hand, the more generic and flexible unsupervised machine learning methods currently produce such complex results, that the domain experts are not capable of assessing the results in a natural and efficient manner. In this paper, we present a novel framework with which SM-EI field practitioners can interact with medical ecosystem data, and assess the results of such complex unsupervised SM-EI algorithms. The assessment framework and the unsupervised epidemic event detection algorithm have been fully implemented and a quantitative study is presented to show the validity of the new approach to SM-EI.}, added-at = {2012-12-11T12:06:32.000+0100}, author = {Stewart, A. and Herder, E. and Smith, M. and Nejdl, W.}, biburl = {https://www.bibsonomy.org/bibtex/2019c68ad8e565f9219709cfd7e57841e/eelcoherder}, booktitle = {Digital Ecosystems and Technologies Conference (DEST), 2011 Proceedings of the 5th IEEE International Conference on}, description = {IEEE Xplore - A user study on public health events detected within the medical ecosystem}, doi = {10.1109/DEST.2011.5936610}, interhash = {ce273f12a8abb99a73c0b2a45b19d74f}, intrahash = {019c68ad8e565f9219709cfd7e57841e}, keywords = {2011 events health m-eco myown public}, month = {31 2011-june 3}, pages = {127 -132}, timestamp = {2012-12-11T12:06:32.000+0100}, title = {A user study on public health events detected within the medical ecosystem}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=5936610}, year = 2011 }