NLPReViz: an interactive tool for natural language processing on clinical text.

The gap between domain experts and natural language processing expertise is a barrier to extracting understanding from clinical text. We describe a prototype tool for interactive review and revision of natural language processing models of binary concepts extracted from clinical notes. We evaluated our prototype in a user study involving 9 physicians, who used our tool to build and revise models for 2 colonoscopy quality variables.

Semantic annotation of clinical events for generating a problem list.

We present a pilot study of an annotation schema representing problems and their attributes, along with their relationship to temporal modifiers. We evaluated the ability for humans to annotate clinical reports using the schema and assessed the contribution of semantic annotations in determining the status of a problem mention as active, inactive, proposed, resolved, negated, or other. Our hypothesis is that the schema captures semantic information useful for generating an accurate problem list.

Building an automated SOAP classifier for emergency department reports.

Information extraction applications that extract structured event and entity information from unstructured text can leverage knowledge of clinical report structure to improve performance. The Subjective, Objective, Assessment, Plan (SOAP) framework, used to structure progress notes to facilitate problem-specific, clinical decision making by physicians, is one example of a well-known, canonical structure in the medical domain. Although its applicability to structuring data is understood, its contribution to information extraction tasks has not yet been determined.