Re: CAP use case - Reasoning on Weighted Condition and Fuzzy Reasoning?

Hi, Chimezie

Yes, let's discuss in detail of possible approaches at our F2F meeting 
next week. 

I was also considering something similar to your following proposal.  But 
one obvious drawback of this approach is that the weights you calculated 
or assigned are very much local context dependent, also, could lead to 
non-monotonic characterises of your KB (i.e. adding new facts could change 
your weights assigned to variables).  This could seriously compromise the 
benefit of SW technology in this area. 

Helen. 
 

>The result of the targetted analysis is a multi-variable risk factor 
>equation (with a very high level of predictive accuracy) that takes:

>- a set of weights for each variable (the weights are 'built-in' to the 
equation)
>- raw patient data

>The equations result in outcome plots that indicate the likelyhood of 
>survival (or the resumption of a particular symptom, effect on ability to 

>work, etc..) at some point in time (by a percentage).
>




Chimezie Ogbuji <ogbujic@bio.ri.ccf.org> 
09/26/2006 09:49 AM

To
Helen Chen/AMPJB/AGFA@AGFA
cc
cebarr01@yahoo.com, aziz@boxwala.com, sam.brandt@siemens.com, 
THONGSERMEIER@partners.org, davide@landcglobal.com, DAN.RUSSLER@oracle.com
Subject
Re: CAP use case







On Tue, 26 Sep 2006 helen.chen@agfa.com wrote:
> Notice in the step 3, it says:
>
> 3: "Obtain chest X-ray, especially if patient has two or more of these
> signs:
>   Temp > 100F
>   Pulse > 100
>   Decreased breath sounds
>   Rales
>   Respiratory rate > 20
>
> Now we are facing the new problem of modelling "two or more" facts of a
> necessary condition for "order chest X-ray" in the knowledge base.

This is definately a problem, I can't see how you would model that using 
either qualified cardinalities in DL or a rule function - most of the 
examples where I've seen counts within a rule LHS involves counts of 
items in an RDF collection.

Perhaps we should consider having a liaison with the Rule Interchange 
Group (http://www.w3.org/2005/rules/wg) for requirements such 
as these?  I think they would benefit from the specific usecase and we 
would benefit from the additional expertise.

> Furthermore, doctors will likely tell you that no only they need to
> express "at least two or more", they also want to express "fact A 
carries
> more weight or more indicative to a diagnosis than fact B".  If we were 
to
> model these "weighted condition", we are opening a whole can of new 
worms,
> and I don't think any SW reasoners now can do reasoning on this.

Definately, can't manage uncertainty or do any fuzzy reasoning in SW. 
However, there is an alternative approach to managing uncertainty that I 
was hoping to discuss during the Fact-to-Fact, is mentioned in the CABG 
usecase, and is the way we go about clinical research here.

Primarily we conduct targetted studies coordinated between our 
biostatisticians and resident physicians.  The physicians identify the 
relevant data points that they believe are primary factors in a 
particular clinical pathway and the statisticians are responsible for the 
statistical merits of the study (minimize noise, ensure all the 
relationships between the variables are covered, etc..).

The result of the targetted analysis is a multi-variable risk factor 
equation (with a very high level of predictive accuracy) that takes:

- a set of weights for each variable (the weights are 'built-in' to the 
equation)
- raw patient data

The equations result in outcome plots that indicate the likelyhood of 
survival (or the resumption of a particular symptom, effect on ability to 
work, etc..) at some point in time (by a percentage).

Such an approach limits the uncertainty factors and weights to the 
'black-box' equation - which results from a targetted statistical / domain 

analysis - such that the remaining pattern matching can be handled by a 
rule-based system.  The suggestion is that factors of uncertainty are 
better managed as the result of a targetted (and therefore responsible) 
statistical analysis that results in a mathematical model than as part of 
an
  adaptable clinical pathway or protocol.   The caviat ofcourse is that 
the rule-system the adaptable 
clinical pathway & protocol is built on must support a logic that includes 

functions in it's syntax.

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