A User Study on Public Health Events Detected within the Medical Ecosystem

verfasst von

Avaré Stewart, Eelco Herder, Matthew Smith, Wolfgang Nejdl

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.

Organisationseinheit(en)

Forschungszentrum L3S

Typ

Aufsatz in Konferenzband

Seiten

127-132

Anzahl der Seiten

6

Publikationsdatum

02.11.2011

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Computergrafik und computergestütztes Design, Computernetzwerke und -kommunikation, Environmental engineering

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1109/DEST.2011.5936610 (Zugang: Unbekannt)