Title :
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
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;
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
DOI :
10.1109/HISB.2012.31
