  Natural Language Processing (NLP) systems often make use of machine learning
techniques that are unfamiliar to end-users who are interested in analyzing
clinical records. Although NLP has been widely used in extracting information
from clinical text, current systems generally do not support model revision
based on feedback from domain experts.
  We present a prototype tool that allows end users to visualize and review the
outputs of an NLP system that extracts binary variables from clinical text. Our
tool combines multiple visualizations to help the users understand these
results and make any necessary corrections, thus forming a feedback loop and
helping improve the accuracy of the NLP models. We have tested our prototype in
a formative think-aloud user study with clinicians and researchers involved in
colonoscopy research. Results from semi-structured interviews and a System
Usability Scale (SUS) analysis show that the users are able to quickly start
refining NLP models, despite having very little or no experience with machine
learning. Observations from these sessions suggest revisions to the interface
to better support review workflow and interpretation of results.
