Difference between revisions of "User-Generated Health Content for Annotations in Watson"
| Line 9: | Line 9: | ||
|Company=IBM | |Company=IBM | ||
|Thesis title=User-Generated Health Content for Annotations in Watson | |Thesis title=User-Generated Health Content for Annotations in Watson | ||
| − | |Finished= | + | |Finished=Yes |
|Thesis=Thesisdesign.pdf | |Thesis=Thesisdesign.pdf | ||
|Poster=thesisdesign-slides-hsk390.pdf | |Poster=thesisdesign-slides-hsk390.pdf | ||
Latest revision as of 07:23, 9 December 2014
| has title::User-Generated Health Content for Annotations in Watson | |
|---|---|
status: finished
| |
| Master: | project within::Information Sciences |
| Student name: | student name::Harriëtte Smook |
| Dates | |
| Start | start date:=2014/01/01 |
| End | end date:=2014/07/01 |
| Supervision | |
| Supervisor: | Lora Aroyo |
| Second supervisor: | Robert-Jan Sips |
| Second reader: | has second reader::Chris Welty |
| Company: | has company::IBM |
| Thesis: | has thesis::Media:Thesisdesign.pdf |
| Poster: | has poster::Media:thesisdesign-slides-hsk390.pdf |
Signature supervisor
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Abstract
IBM's Watson in healthcare serves as a clinical decision support system. Watson is currently being trained using annotations made by medical experts via the Dr. Detective game, as well as by the general crowd via micro-tasks on Amazon Mechanical Turk and CrowdFlower. These annotations come from sentences that are extracted from specialized medical texts. Nevertheless, patients often use more lay language than medical terminology, and crowdsourcing annotations via Amazon Mechanical Turk and CrowdFlower has drawbacks.
This project will investigate whether social health websites such as PatientsLikeMe, CureTogether, and MedHelp can function as alternative resources for medical data, as well as alternative platforms for crowdsourcing micro-tasks.
