Cardiovascular Disease Symptoms
Synopsis
This query uses recursion and the Snomed CT concept hierarchy to find all the child concepts for cardiovascular disease. Using the Experiencer relaton, this query restricts the results to only those mentions affecting the patient herself.
Processing Requirements
This query assumes that at least the following processing options were enabled:
--feature experiencer-relation, snomed-ontology--store-sections-and-sentences
For this query, Clinical notes containing HISTORY OF PRESENT ILLNESS section were processed with the following document type:
--category Clinical--subcategory generic
Additionally, this query requires the snomed relationships table. This table can be imported into the Output Database following the instructions provided here.
Clinical Context
Due to the complexity of human biology, there are many disease states; SNOMED has encoded many hundreds of thousands of concepts referring to diseases organizing them hierarchically. For example, the concept 49601007 |Disorder of cardiovascular system (disorder)| is a parent concept to 52 child concepts, one of which is 56265001 |Heart disease (disorder)|, which itself is a parent concept to nearly 12,000 additional child concepts when each branch in the SNOMED hierarchy below 56265001 is followed to the leaf concepts.
As there are many medical synonyms, and subtypes of disease, and the NLP API will always try to identify mentions in clinical text with the highest precision possible - i.e. emtellipro tries match the most specific disorder subtype by returning the longest match. This is a good thing, because if a clinician writes that a patient has left posterior fascicular block, the NLP API will code this to 62026008 |Left posterior fascicular block (disorder)| preserving as much information from the text as possible. However, this behaviour makes finding general disease references more challenging.
If a clinician or researcher wants to know if a patient has any type of heart disease, it would be impractical to write a SQL query that specifies all 12,000 concepts related to heart disease. Instead, they would rather write a query for a single concept in the SNOMED hierarchy and find matches for that concept and its children concepts that are in the SNOMED hierarchy.
emtelligent has enabled this functionality by allowing users to add the SNOMED relationship table (sct_relationship) to their Output Databases. When the sct_relationship table has been added to an Output Database, the database then has ‘knowledge’ of the SNOMED hierarchy, and can perform hierarchical queries to identify any concept mentions at and below a given level in the hierarchy. The query below uses this add-on table and recursive search functionality in SQL to perform this very broad type of search by querying for all child concepts for:
49601007 |Disorder of cardiovascular system (disorder)|
SQL query
SQL
Snowflake
MS SQL Server
Next steps
You can apply this query pattern to find related symptoms for various disease conditions or syndromes of interest.

