Crowdsourcing Proves Effective For Labeling Medical Terms

Susan D. Hall | FierceHealthIT | May 6, 2013

Crowdsourcing can be an effective means of labeling medically relevant terms that could then be used in statistical tools to provide sentence-level context results, a study from Stanford University found. 

Though many studies in natural language processing are aimed at parsing content from doctors' free-text notes, this new research aims to employ non-expert humans to label content that patients post online. The study is published at the Journal of the American Informatics Association.

The researchers farmed out the task of labeling medically relevant terms in text to Amazon's Mechanical Turk service, where workers perform small tasks for a fee. Their performance was compared with that of registered nurses contracted through the online site ODesk. They found the results to be acceptably similar, with the crowdsourcing costing far less than the nurses.