- From: Ivan Herman <ivan@w3.org>
- Date: Thu, 28 Sep 2006 11:26:46 +0200
- To: helen.chen@agfa.com
- CC: marshall@science.uva.nl, public-semweb-lifesci@w3.org
- Message-ID: <451B9556.3040504@w3.org>
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