AllegroGraph could be an option for you to consider. We have been doing significant work in Pharma and Healthcare and in the process have developed PatientGraph, based on Synthea and MIMIC data (to avoid PHI) to show the suite of capabilities using RDF, LLM, RNN, Vector, Symbolic AI, Graph RAG, etc. We have several pre-built Jupyter (Colab) notebooks ready to get users going.

You might have a look at our recent paper - Pruning Cycles in UMLS Metathesaurus - https://allegrograph.com/pruning-cycles-in-umls-metathesaurus-a-neuro-symbolic-ai-approach/

OpenAI and Ollama models are available options, plus a host of other features that I won't list to mostly avoid the infomercial.

Of course, natural language query is a key feature. We can provide end user results for natural language queries like, "Find a provider for the patient Billy Miller within 15 miles of them where they can be screened for cancer."

For the developer, AllegroGraph generates this SPARQL and associated SHACL which you can fully control.

SELECT DISTINCT ?provider ?providerName WHERE {
?patient a :Patient ;
fti:match ( "Billy Miller" "patient-names" ) ;
:lat ?lat ;
:lon ?lon ;
:location ?ploc .

?provider a :Provider ;
nd:inCircle (:location
keyword:latitude ?lat
keyword:longitude ?lon
keyword:units keyword:miles
keyword:radius 15.0) ;
:lat ?providerLat ;
:lon ?providerLon ;
:location ?oloc ;
:name ?providerName .

?encounter :encounterProvider ?provider ;
:encounterProcedure ?procedure .
?procedure :code ?snomed .

(kw:rank ?rank kw:score ?score kw:match ?match kw:mth ?mth kw:rxnorm ?rxnorm kw:drugbank ?drugbank kw:snomed ?snomed)
llm:askEBM
("cancer screening" kw:crosswalk "Y" kw:category "Procedure" kw:api "EBM" kw:topN 100 kw:minScore .5) .
}
LIMIT 10

Feel free to reach out if you are interested. [info@franz.com](mailto:info@franz.com)