Concept Queries

The examples in this section are a good starting point if this is your first exposure to the structured output of the NLP API. These examples show how to find entities (i.e. recognized terms) by concept IDs and entity types, using various supported ontologies.

After finding the entities, you can examine their attributes as follows:

• polarity: whether they are asserted or negated
• certainty: whether the assertion or negation is certain or uncertain
• section_name: the normalized section name corresponding to the report subsection where the term was found
• known_ambiguity: whether this term can be unambiguously mapped to a single concept or if ambiguity in common usage which does not allow it to be mapped definitively to a single concept definition.

Other advance query patterns build on these. Hence, it is recommended you start with these queries if you are new to the NLP API.

Some queries in this section also show how to use the experiencer relation to constrain the results returned to those entities that affect the patient himself (and not other persons or concepts such as the patient’s family or family history).

More information about the experiencer relation feature is found here.