Cheap, Fast, and Good Enough for the Non-biomedical Domain but is It Usable for Clinical Natural Language Processing? Evaluating Crowdsourcing for Clinical Trial Announcement Named Entity Annotations

Author

Zhai, Haijun ; Lingren, Todd ; Deleger, Louise ; Li, Qi ; Kaiser, Megan ; Stoutenborough, Laura ; Solti, Imre

Author_Institution

Div. of Biomed. Inf., Cincinnati Children´´s Hosp. Med. Center, Cincinnati, OH, USA

fYear

2012

fDate

27-28 Sept. 2012

Firstpage

106

Lastpage

106

Abstract

Building upon previous work from the general crowdsourcing research, this study investigates the usability of crowdsourcing in the clinical NLP domain for annotating medical named entities and entity linkages in a clinical trial announcement (CTA) corpus. The results indicate that crowdsourcing is a feasible, inexpensive, fast, and practical approach to annotate clinical text (without PHI) on large scale for medical named entities. The crowdsourcing program code was released publicly.

Keywords

information retrieval; medical computing; natural language processing; outsourcing; text analysis; CTA corpus; clinical NLP domain; clinical natural language processing; clinical text annotation; clinical trial announcement corpus; crowdsourcing evaluation; crowdsourcing program code; crowdsourcing usability; entity linkages; medical named entity annotation; Biomedical imaging; Clinical trials; Hospitals; Joining processes; Natural language processing; Pediatrics; Usability;

fLanguage

English

Publisher

ieee

Conference_Titel

Healthcare Informatics, Imaging and Systems Biology (HISB), 2012 IEEE Second International Conference on

Conference_Location

San Diego, CA

Print_ISBN

978-1-4673-4803-4

Type

conf

DOI

10.1109/HISB.2012.31

Filename

6366196