Thomas Beale a ?crit : >> >> My feeling is that the good order to ask questions (and answer it) is : >> Why do you want to communicate ? >> What discourse complexity level can allow to address these needs ? >> What discourse representation technology fits these "required >> language" ? > > I think it is problematic to think only in terms of linguistic > discourse; we need to think in terms of conceptual truths as well. > Infection with plasmodium parasite leads to malaria, no matter what > language it is written in; certain kinds of polyps in the colon > increase the risk of bowel cancer, no matter what language. > "Ontologies of reaility" as I call them should be (more or less) > linguistically independent. The problem is to agree on one or more > good quality ones. Then archetypes have something to work with. > Unfortunately, I fear that the Snomed-ct developers are losing sight > of what a good terminology is and trying to jam many incorrect > concepts into it.
Hi Tom, Aren't we talking on orthogonal axis ? What I meant with "discourse structure" is, for example, the formal way you can describe a colonoscopy report. It is a huge Archetype, or rather a set of linked Archetypes. The reason why I call it "discourse structure" is because I really think it is important to see any medical information as a "story part", in some kind of fractal way : the polyp (and its description, maybe its ablation) is a very small story inside the "colonoscopy" larger story, inside the "patient history" journey. So, thinking in term of "discourse structure" just means you envision all this, and take care to be homogeneous at any level. Of course, this terminology is close from the natural language one. My vision is that a good natural language translation system must have two levels : an "analysis layer" that transform the input discourse into a formal language independent representation (FLIR), than a "synthesis layer" that transform this FLIR into an output natural language. In my opinion, our goal really is to see the medical information we store as a FLIR. The FLIR is made from a discourse structure populated with terms from an ontology. This ontology can be: a concept list (level 0 : no predicate) a semantic network (level 1 : level 2 predicates : isA(colitis, inflammatory disease)) or more complex relationships ; I think that your "Ontology of reality" is of the kind. Currently, I must confess that my ideas are just clear enough concerning the semantic network. Because it is a genuine "root component" you can use everywhere. From my point of view, more complex ontologies become very specific to a given decision support system (DSS) technology. To be more accurate, for the examples you gave, you could want to address the follow up of this "bad polyp" by instantiating a Bayesian network or just a decision tree. The formal representation is much different. My idea has been to build a "formal description", like the FLIR of a medical encyclopedia entry for this polyp. It is (quite) easy and useful for drugs, because you can get standard chapters such as "indications, contra-indications, bad interactions". But for symptoms or diseases, it is much harder. Currently I decided no longer to work on that, and use a "band" of smart agents, each one with its specific knowledge and its specific DSS technology, rather than building a more general expert system using a level X ontology. Well, quite a long post. Sorry to take your time when you release V 1.0. And felicitations to the openEHR team for this. Cheers, Philippe
