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

Hi, Vipul

Strongly agreed with two points you made here. 

The problem, however, is first on the modelling side of such "weighted 
conditions".  In the example, 

> 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

these 5 signs are all indicative to a diagnosis of pneumonia, but carry 
different weights in decision making.  The weight you assign to a symptom 
here is local to the context (suspected pneumonia).  When multiple 
guidelines are taking into consideration to form a pathway for a patient, 
these weights will likely to change, isn't it?  How your KB handle it?

Kind regards.

Helen






"Kashyap, Vipul" <VKASHYAP1@PARTNERS.ORG> 
Sent by: public-semweb-lifesci-request@w3.org
09/26/2006 05:42 PM

To
Helen Chen/AMPJB/AGFA@AGFA, <ogbujic@bio.ri.ccf.org>
cc
<public-semweb-lifesci@w3.org>
Subject
RE: CAP use case - Reasoning on Weighted Condition and Fuzzy Reasoning?







This is a very good use case which can easily be done using the 
statistical/linear programming approach
where you create equations out of various decision variables  The two 
downsides of this approach are as follows:
 
1.      Lack of explanation capabilities: A key feature for clinical 
decision support is that physicians like to get
explanations for the recommendations proposed by the system.
2.      Lack of ?knowledge visibility?: The biggest downside is from the 
KM perspective, what if one of the conditions changes? We need this to be 
visible so that we can have KM processes handle these changes.
 
As far s handling fuzzy situations using rule bases, a ?hackers solution? 
would be to have specialized global variables keeping 
track of the number of conditions matched, etc  Of course some variant of 
this has been implemented in the MYCIN type of 
expert systems?. one needs to tread carefully:
 
BTW, current rule engines support rule priorities and one could model the 
most specific rule as having higher priority than others, for instance
 
IF A1 and B1 Then C1 will have a higher priority then
IF A1 Then C2 or 
IF B1 Then C3?.
 
Of course these requirements need to make it into SW standards ?
 
 
 
=======================================
Vipul Kashyap, Ph.D.
Senior Medical Informatician
Clinical Informatics R&D, Partners HealthCare System
Phone: (781)416-9254
Cell: (617)943-7120
http://www.partners.org/cird/AboutUs.asp?cBox=Staff&stAb=vik
 
To keep up you need the right answers; to get ahead you need the right 
questions
---John Browning and Spencer Reiss, Wired 6.04.95

From: public-semweb-lifesci-request@w3.org 
[mailto:public-semweb-lifesci-request@w3.org] On Behalf Of 
helen.chen@agfa.com
Sent: Tuesday, September 26, 2006 11:05 AM
To: ogbujic@bio.ri.ccf.org
Cc: public-semweb-lifesci@w3.org
Subject: 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