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

Just a small point. The organizers of the URSW workshop of Athens have
just asked me to give a short (15 minutes) keynote. It would be great if
I could use a real use case to emphasize the points Scott has made (not
only a mock-up one). Can I hope for something like that right after the
f2f? (The workshop is early November)

Ivan

helen.chen@agfa.com wrote:
> 
> Hi, Scott
> 
> Good point.  Here are more details about the "weighted conditions" in
> the same use case.
> 
> In the CAP guideline [1], the following table is presented to present
> the various factors of these signs:
> 
> Multivariate Predictors of Pneumonia, with logistic Coefficient, Odds
> Ratios, and 95% CIs
> 
> Variable                    LogisticCoefficient     OddsRatio        95% CIs
> ---------------------------------------------------------------------------------
> Temperature > 100F         0.494                     2.69          1.73
> to 4.17
> Pulse > 100 beats              0.428                     2.35        
> 1.52 to 3.65
> Rales                                  0.658                    3.73    
>      2.43 to 5.72
> Decreased breath sounds    0.638                    3.58          2.33
> to 5.50
> Absence of asthma              0.691                    3.98        
>  1.89 to 8.42
> Intercept                              -1.705                   0.18    
>      0.12 to 0.26
> 
> [1]
> http://www.guideline.gov/summary/summary.aspx?doc_id=9398&nbr=005034&string=community+AND+acquired+AND+pneumonia
> 
> 
> Helen
> 
> 
> 
> 
> *"M. Scott Marshall" <marshall@science.uva.nl>*
> 
> 09/27/2006 08:38 AM
> 
>  
> To
>  "Kashyap, Vipul" <VKASHYAP1@PARTNERS.ORG>
> cc
>  Helen Chen/AMPJB/AGFA@AGFA, public-semweb-lifesci@w3.org
> Subject
>  Re: CAP use case - Reasoning on Weighted Condition and Fuzzy Reasoning?
> 
> 
>  
> 
> 
> 
> 
> 
> 
> Nice workaround, Vipul.
> 
> I have to agree with you that it has a few problems. As you pointed out,
> it doesn't scale well. Another problem is that the "black & white" view
> provided by so-called crisp logic of DL is insufficient to represent or
> reason with all the knowledge available to us and can create "brittle"
> systems. Weights have already been mentioned but another thing missing
> is a way to evaluate "close calls". For example, what if the patient has
> a pulse and temperature of 99 (just under the threshold)? What if we can
> quantify the decrease in breath sounds? Questions like these also
> motivate statistical approaches such as probabilistic reasoning.
> 
> If this interests people on this list that will be in Athens for the
> ISWC in November, consider going to the Workshop on Uncertainty
> Reasoning for the Semantic Web[1]. See also Rule Interchange Format
> (RIF) Use Cases and Requirements W3C Working Draft[2]. HCLS could
> eventually become a rich source of use cases that would help to motivate
> support for uncertainty in workgroups like RIF.
> 
> -scott
> 
> [1]http://www.iet.com/iswc/2006/ursw/
> [2]http://www.w3.org/TR/rif-ucr/
> 
> -- 
> M. Scott Marshall
> http://staff.science.uva.nl/~marshall
> http://integrativebioinformatics.nl/
> Integrative Bioinformatics Unit, University of Amsterdam
> 
> Kashyap, Vipul wrote:
>> Here’s a potential solution, though I am not completely happy with this
>> solution.
>>
>>  
>>
>> Define a state called “TwoOrMoreConditions” and define the following
> rules:
>>
>>  
>>
>> IF Temp > 100F AND Pulse > 100 THEN TwoOrMoreConditions = true
>>
>> IF Pulse > 100 AND Decreased Breath Sounds THEN TwoOrMoreConditions = true
>>
>> IF Decreased Breath Sounds AND Rales THEN TwoOrMoreConditions = true
>>
>> ….
>>
>>  
>>
>>  
>>
>> Then define a rule which checks for this state to be true:
>>
>> IF “TwoOrMoreConditions” is true THEN order an X-Ray
>>
>>  
>>
>>  
>>
>> This could be represented as an OWL definition
>>
>>  
>>
>> You may want to start of with defining the following XML Schema datatypes
>>
>> TempGreaterThan100
>>
>> PulseGreaterThan100
>>
>> RespiratoryRateGreaterThan100
>>
>>  
>>
>> and the following OWL definitions
>>
>> equivalentClass(PatientWithDecreasedBreathSounds,
>> intersectionOf(Patient, Restriction(breathingIntensity,
> hasValue(reduced))))
>>
>> equivalentClass(PaitentWithRales, intersectionOf(Patient,
>> Restriction(condition, hasValue(Rales))))
>>
>>  
>>
>> equivalentClass(TwoOrMoreConditions,
>> unionOf(intersectionOf(TempGreaterThan100, PulseGreaterThan100),
>>
>>                                                                      
>> intersectionOf(PulseGreaterThan100, PatientWithDecreasedBreathSounds)
>>
>>                                                                      ….)
>>
>>  
>>
>> DL Reasoners support datatype reasoning.…
>>
>>  
>>
>> The problem with this approach is the combinatorial complexity of
>> authoring the rule base…
>>
>> So for this case, you would need 5C2 rules, i.e., 10 rules to model this…
>>
>>  
>>
>> Another approach to reduce the rule authoring load is to define a
>> “macro” Atleast K are true
>>
>> and then expand this macro behind the scenes. Of course need to understand
>>
>>  
>>
>> This is one of the motifvations for going to statistical/linear
>> programming approaches.
>>
>>  
>>
>> Feedback, suggestions welcome.
>>
>>  
>>
>> ---Vipul
>>
>>  
>>
>> ------------------------------------------------------------------------
>>
>> *From:* helen.chen@agfa.com [mailto:helen.chen@agfa.com]
>> *Sent:* Wednesday, September 27, 2006 5:05 AM
>> *To:* Kashyap, Vipul
>> *Cc:* public-semweb-lifesci@w3.org; public-semweb-lifesci-request@w3.org
>> *Subject:* 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
>> <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
>>
> 
> 
> 
> 

-- 

Ivan Herman, W3C Semantic Web Activity Lead
URL: http://www.w3.org/People/Ivan/
PGP Key: http://www.cwi.nl/%7Eivan/AboutMe/pgpkey.html
FOAF: http://www.ivan-herman.net/foaf.rdf

Received on Thursday, 28 September 2006 09:26:26 UTC