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

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
>