1. Yes, as Chintan said, in the case where you had explicit
negations in the data (e.g., the lab data rules out the presence of a
certain infectious agent), you clearly want to use open world
reasoning. However, if someone is not explicitly asserted to be on
some prescription drug, it is fair to assume that they are not taking
the drug (closed world assumption).
2. I tend to think this comes from an understanding of the domain
(unfortunately), and what you are modeling rather than the data
characteristics per se.
3. There is some elegant work by Boris Motik and others which has
addressed the issue of combining the two although I don't know what
the status is on implementation. (http://www.webont.org/owled/2005/
sub12.pdf). In terms of whether you can do this using SQL querying
alone, based on our experience, its unlikely. The problem is that
the types of clinical exclusion and inclusion criteria we saw on
clinicalTrials.gov cannot be easily reduced to SQL querying (at least
with the structured medical records we got from Columbia). From
discussions with other institutions, we know this isn't unique to
Columbia (i.e., there is a substantial "semantic gap" between what's
in the structured record and what is being queried by investigators
for clinical trials).
Kavitha
IBM Research
On Sep 12, 2007, at 9:14 AM, Kashyap, Vipul wrote:
Agree. The assumption is that the user will choose whether it is
closed world or open world. The key point that we've observed in
terms of our clinical trials matching work using ontologies is that
you need BOTH options to be available to correctly translate the
exclusion criteria into DL queries.
[VK] The key issues we may want to explore in the context of a well
defined use
case (which hopefully mirrors the real world to a significant
extent) are:
1. How do we decide when to use OWA vs CWA?
2. Is it possible to look at the data characteristics to determine
that? For
instance, Chintan's lab example where negative statements are
explicitly
asserted.
3. Use of appropriate technology for the same. For instance, we may
want to
articulate the pros and cons of using a SQL querying based approach
as opposed
to an OWL classification based approach.
---Vipul
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