RE: [BIONT-DSE] Inclusion versus exclusion criteria

> For example, in pharmacy data, if the patient record does not mention
> a drug, we can be reasonably sure that the patient is not on that
> drug -- a case for closed world reasoning, whereas for other datasets
> such as lab or radiology, often things are explicitly asserted to be
> negative if not present, for example, negative MRSA results, hence
> requiring an open world reasoning approach.

[VK] Thanks for the above examples ... I guess we should make sure that 
the use case which Rachel is developing contains some examples of medications
and labs. 

I guess the issue then becomes for which data items/decision criteria is
negation explicitly asserted (MRSA) vs it needs to be inferred (drugs)

Also, is it the case that one can make this statement about all labs without
loss of generality? Or can this be said only in a contextual manner, i.e.,
Negative labs are explicitly asserted only for a given set of lab results
as reported by a given set of diagnostic laboratories?

> We also found that implementing exclusion criteria as queries is
> simply not a matter of OR-ed negations, atleast within the Semantic
> Web framework. In description logics, generally the ABox queries are
> negated and then added to the knowledge base to find the matching
> individuals (by contradiction), so if our query itself is negated,
> internally the reasoner will negate it again and hence we ll not find
> any matching results. 

We are still trying to finalize the use case, but looking a little bit ahead,
there are the following possiblities:
- SQL query based matching
- OWL based classification
- Rules based classification
Your observations above will help us identify the right technological
choices for this use case. May be the answer could be some hybrid strategy.

> So the solution we used was to perform a set
> difference between the patients matching the inclusion and exclusion
> criteria.

[VK] Isn't this equivalent to the closed world assumption? The reason being that
if it is not known that a patient satisfies the exclusion criteria, then the
patient will not appear in the second list, So, if the patient satisfies the
inclusion criteria, then he will be selected for the trial.


On the other hand, the open world assumption would require to makes sure that
"it is not the case that the patient does not satisfy the exclusion criteria"

So, in the open world assumption case, you would probably need to compute
(Patients satisfying Inclusion Criteria) DIFFERENCE 
(All Patients DIFFERENCE Patients Not satisfying the Exclusion Criteria)

I am not sure if this what you intended to do but you might run into trouble
if labs appeared in the exclusion criteria. 

Does this make sense, or am I missing something here?

---Vipul


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Received on Wednesday, 12 September 2007 03:03:20 UTC