Identifying medical terms in patient-authored text: a crowdsourcing-based approach.

ABSTRACT:

Background and objective

As people increasingly engage in online health-seeking behavior and contribute to health-oriented websites, the volume of medical text authored by patients and other medical novices grows rapidly. However, we lack an effective method for automatically identifying medical terms in patient-authored text (PAT). We demonstrate that crowdsourcing PAT medical term identification tasks to non-experts is a viable method for creating large, accurately-labeled PAT datasets; moreover, such datasets can be used to train classifiers that outperform existing medical term identification tools.

Materials and methods

To evaluate the viability of using non-expert crowds to label PAT, we compare expert (registered nurses) and non-expert (Amazon Mechanical Turk workers;

SUBMITTER: MacLean DL 

PROVIDER: S-EPMC3822103 | biostudies-literature | 2013 Nov-Dec

REPOSITORIES: biostudies-literature