- From: Chimezie Ogbuji <ogbujic@bio.ri.ccf.org>
- Date: Wed, 28 Jun 2006 08:09:52 -0400 (EDT)
- To: w3c semweb hcls <public-semweb-lifesci@w3.org>
I've found those acronyms (including what we refer to, here as the Computerized Patient Record) do more to cause confusion than anything else. Perhaps there is some historical context that I'm not aware of, but here in Cleveland Clinic's Cardiothoracic Surgery Research department we've been developing a domain-specific model to accommodate our 36+ year-old relational database (tracking over 105,000 patient actively) for cardiothoracic procedures, demographic data, angiogram measurements, laboratory tests, and clinical diagnoses. Tbe model is expressed in an RDF vocabulary (a hybrid combination of SKOS, OWL, RDFS, XSD) that facilitates automated dual-representation of instance data as XML and RDF using XSLT . This has allowed us to generate user interface dialects that understand XML (XForms primarily) automatically for data entry a separate, conversation thread altogether :) Automation and the ability to accommodate rapid growth into unanticipated domains of medicine have been the primary research goals. Since we didn't find any good precedent to follow (i.e., an *accessible* OWL or RDFS upper model of a patient record that could also express domain-specific concepts with precision), we used the vocabulary to express a model domain sufficient to support our research vehicle. In the end it was cheaper than taking the proverbial top-down approach. Our model has been fashioned by domain experts with experience in the primary day-to-day requirements of the data, so for concepts within the domain of cardiothoracic procedures and diagnoses it is well modeled. For other domain-agnostic concepts (such as temporal relationships, logical composition, demographic terms, etc..) there may have been other alternatives but links can be easily expressed with description logics (DL) mechanisms or brute force first order logic (FOL) implication rules. It seems to me like the 'Electronic' and 'Computer' terms in those acronyms describe the general advantage of storing health care information as variables and values instead of hardcopy and / or narrative rather than a modeling convention of some any kind. When you consider the expressiveness of DL (OWL) and FOL (N3 and RDF), the specific set of problems your data is meant to address becomes a more prominent issue especially if you have a good set of modeling best practices to follow consistently (one of the major missing components in SW literature). Personally, I think when well-conceived and well-documented modeling patterns guide bottom-up vocabulary building you get more bang for your buck with SW technologies - so to speak. This can be as simple as an overview of the core components / mechanisms of DL with accessible examples. I've seen too many OWL ontologies that aren't as expressive as they can be about their classifications and are essentially flat taxonomies. At some point, you pay the price for not taking advantage of the expressiveness of DL upfront: either while attempting to draw equivalence (owl:sameAs, owl:equivalentClass, etc..) links with other vocabularies or when composing 'raw' implication rules. >From my brief perusal, the HL7 V3 RIM model seems precise and concise. We could probably get good mileage out of modeling a subset of it in OWL to accommodate fleshing out the scenario for using inference with stroke management decision support. With regard to rules, I have a specific interest in investigating the use of forward-chaining inference to derive variables for unanticipated clinical study criteria, explicit temporal reasoning (see article below on using N3 for temporal reasoning), and in using backward-chaining for augmenting experimental hypothesis with logical proofs - an area I believe AGFA's Euler Path mechanism is well equipped for. [1] Practical Temporal Reasoning with Notation 3: http://copia.ogbuji.net/blog/2006-04-24/practical_temporal_reasoning_with_N3 [2] AGFA's Euler Proof Mechanism: http://www.agfa.com/w3c/euler/ [3] Desciption Logics Basics, Applications, and More: http://www.cs.man.ac.uk/~horrocks/Slides/ecai-handout.pdf On Tue, 27 Jun 2006, Eric Neumann wrote: > > Helen, Chimezie, > > That's great news! If I am understanding you, I see some possibilities for > taking one of the EPR scenarios and adding some SW structure into them to > augment the scenario with querying and inferencing for decision support... > > btw, can you help me (and others) understand how EPR (electronic patient > record) is related to EMR (electronic medical record) and EHR (electronic > health record) ? Maybe we should set up a glossary on our HCLS site... > > On a related note, I'd like to mention that an author (George Cole) for one > of the EDC (electronic data capture) proposals for CDISC, HL7 was asking > about SW. They are proposing to use XForms (http://www.w3.org/TR/xforms/), > and it seemed to me this would be an interesting area to connect up with the > RDFA initiative (think XForms with RDFA embedded in them!). We hope to put > this use-case up on our wiki... > > Eric > > Chimezie Ogbuji Lead Systems Analyst Thoracic and Cardiovascular Surgery Cleveland Clinic Foundation 9500 Euclid Avenue/ W26 Cleveland, Ohio 44195 Office: (216)444-8593 ogbujic@ccf.org
Received on Wednesday, 28 June 2006 12:10:09 UTC