Excellent! Let's talk offline about scheduling.
Also, a question: what benefits did you experience in using an
RDF/SPARQL approach as opposed to a relational/SQL approach? (Playing
devil's advocate) Wouldn't the query that you described have been
relatively simple in SQL? If not, why not?
Thanks,
David Booth
On 09/29/2015 11:30 PM, [email protected] wrote:
Even better. I can just present it to you on a webex or another kind of
call.
From: David Booth <[email protected]>
To: Peter Hendler/CA/KAIPERM@KAIPERM
Cc: [email protected], [email protected]
Date: 09/29/2015 03:15 PM
Subject: Re: Using RDF, Datalog, OWL-RL and a "RIM lite ERPA Ontology"
to calculate HEDIS Quality measures
------------------------------------------------------------------------
Excellent! Unfortunately I will miss your presentation. Can we get
your slides? Even better, if your presentation is recorded that would
be awesome.
Thanks,
David Booth
On 09/29/2015 05:38 PM, [email protected] wrote:
> At KP, and working with Ian Horrock's group at Oxford, we have been
> experimenting with their new RDF, Datalog, OWL-RL triple store called
> "RDFox".
>
> We have calculated the HEDIS Diabetes quality measure on a population of
> over 400,000 patients real data.
>
> We still have to compare our numerators and denominators to results
> calculated with SQL and traditional DB tables.
>
> I will be presenting a very simple version of this at HL7 at the AID
> work group in Atlanta on Monday Q3.
>
> I believe this is the first time a complex HEDIS quality measure has
> been calculated with RDF, OWL and Datalog and SPARQL on a large
> population of real patients.
>
> I will not be presenting the complete complex HEDIS measure (which would
> take days), but a smaller example to explain how it all works.
>
> We used SNOMED subsumption to generate a small value set of SNOMED codes
> that are "kinds of Diabetes". Using that SNOMED VS, we found all the
> patients who had a visit coded for Diabetes. Then we searched all of
> their HgBA1C values and then found the "last value". We could then look
> at the numerical results of the HgBA1C and find how many of them were
> below 7% (good control).
>
> In order to do this we had previously created an OWL ontology based on
> Entities in Roles that Participate in Acts. It is not the full HL7 V3
> RIM, but only what was needed for this exercise.
> This "KCOM" model is what we presented before at HL7 AID meetings.
> This entire project would not have been possible to do without first
> mapping the raw clinical data to this ERPA OWL backbone ontology. All
> of our queries were based on this ERPA (Entities in Roles Participating
> in Acts).
>
> RDFox is multi threaded and we were able to run the data materialization
> on 8 threads on an 8 core machine with 64 Gig RAM. It ran in only a few
> hours and we have already found ways to speed it up further.
>
> Hope to see you at HL7 Atlanta.
>
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