Description
Natural language processing (NLP) research has matured to the point of
being used successfully in applications ranging from search to
information extraction to text analytics such as sentiment.
Biomedical NLP has shown great diversity of domains, genres,
methods, tasks, and applications e.g..
In spite of burgeoning research, few, if any, published papers
describe usability or utility of biomedical NLP applications. NLP
has not reached its full potential, and one of the reasons may be that
there is a disconnect between the linguistically-motivated annotations
and the needs of the end users. A central goal of this proposal is to
address that gap.
We propose to directly link the clinical research user with automated
NLP annotations through a general, iterative process correlating the
many layers of annotations with user views of the data. The NLP
annotators (auto-annotators) and the user interface will mutually
inform each other in order to develop an interactive search
application for clinical researchers that increases efficiency in
reviewing retrospective patient data. We will perform formative and
summative evaluations of our NLP-enabled search application, using
four research studies as use cases, and will evaluate the final
application on four unseen use cases. The final application will
comprise a back end for storing annotations and the associated text,
auto-annotators, and user interfaces. The application will be released
as a stand-alone application and with plug-ins for existing clinical
data repositories so that a researcher with an approved research study
can use the tool to search and review the relevant patient records.
This project is funded by the National Library of Medicine.