From: AAAI Technical Report SS-98-04. Compilation copyright © 1998, AAAI (www.aaai.org). All rights reserved.
Reasoning from KnowledgeSupported by Moreor Less Evidence in a
ComputerizedDecision Support System for Bone-MarrowPostTransplant Care
Isabelle Bichindaritz and Keith M. Sullivan
Clinical ResearchDivision
Fred HutchinsonCancerResearchCenter
Seattle, Washington
98105
ibichind@fhcrc.org
Abstract
The systempresented in this paper proposes to implement
the concept of evidence-based medical practice. This
reasoning frameworkpermits physicians to associate
preferencesto the kindof knowledge
they reuse duringtheir
care, favoring knowledgebased uponmoreevidence. This
systemis a computerized
decision-supportassistant for the
long-termfollow-upof bone-marrow
post-transplant care. It
managesthe cooperationof case-basedreasoning,rule-based
reasoning and knowledgeacquisition to solve patient
problems.In this domain,no knowledgesource alone can
address the range of problemsmet, and the cooperationof
the different knowledgesources, such as cases, pathways
and guidelines, providesa muchbetter coverageof boththe
variety andcomplexity
of the different problems
to solve.
Introduction
Evidence-based practice in medicine proposes to
emphasize the performance of medical practice based upon
proven and validated practice. In this article, evidencebased practice is matched to knowledge-based practice.
Although both terms do not represent exactly the same
concept, scientific knowledgeis based upon evidence, and
the medical science grounds its knowledgeupon evidence.
The system presented here is a knowledge-based
computerized decision-support system on the World-Wide
Web (WWW).It permits to assist its users in the
performance of a set of clinical tasks using a general
frameworkfor reasoning from knowledgesources of varied
quality, which means that their knowledgeis based upon
varied evidence. It uses priority rules that permit it to use
preferably widely-recognized knowledge than littlerecognized knowledge. But when well-established
knowledgeis not available, the knowledgeof one expert is
very useful to give some clues about a precise clinical
problemto solve, thus addressing its complexity.
This computerizeddecision-support system is applied to the
Copyright©1997,American
Associationfor Artificial Intelligence
(www.aaai.org).
Allfightsreserved.
85
long-term follow-up (LTFU)of patients having undergone
bone-marrow transplant (BMT)at the Fred Hutchinson
Cancer Research Center (FHCRC)in Seattle, after their
return in their home community. From an artificial
intelligence viewpoint, this systemhas applied mainlycasebased reasoning, knowledgeacquisition, machinelearning,
rule-based reasoning and intelligent information retrieval
methods. This paper focuses on the integration of casebased reasoning, knowledge acquisition and rule-based
reasoningin this system.
Case-based reasoning (CBR) is often presented as
problem-solving methodology grounded upon the reuse of
the solutions of memorizedcases. Nevertheless, case-based
reasoning takes its origin in moreambitious workabout the
modelingof reasoning in general, and so closer to cognitive
modeling than in the previous definition. Therefore the
present system proposes to enrich case-based reasoning
with rule-based reasoning (RBR)and information retrieval
(RI) (this second aspect of the system reasoning is
presented in this paper). For that purpose, it enlarges the
classical CBRframework,for instance by opening solutions
reuse to other types of reuse than the classical adaptation
generally used in CBR,and by reusing rules acquired by
knowledge acquisition. The global system reasoning is
based upon a cooperation of the different knowledge
sources and associated reasoning types in order to solve a
given target patient case.
The first section of this article explains the concept of
evidence-based reasoning in the context of the application
domain. The second section presents case-based reasoning
as it is used as the main methodologyof this system. The
third section presents rule-based reasoning, based on the
RETEalgorithm of production systems. The fourth section
develops the knowledgerepresentation for all the kinds of
knowledgein the system. The fifth section explicitates the
system reasoning. The sixth section discusses this system
approach of multi-modal reasoning and compares it with
other systems. It is followed by the conclusion.
solving situation, some being complex, solved by an
expert. This problem-solving example may have needed
to resort to the expertise of several experts, but it is
essentially a real patient problem-solvingsituation, and
not a standardized, prototypical one as a practice
guideline or a practice pathway.
Evidence-Based Reasoning in BMT
This system proposes to implement the concept of
evidence-based reasoning in the context of bone-marrow
post-transplant care. In medical information systems, as in
other scientific fields, the quality of practice maybe
characterized by two main dimensions of the knowledge
upon whichit is grounded:
Case-Based Reasoning
As a CBRsystem, this system performs a reasoning RcaR by
reusing the elements of a set of previously performed
reasonings, called a case-base, or memoryM, in order to
performa set of cognitive tasks T, for instance a diagnosis
or a treatment planning.
¯ Reliability: whenpractice is grounded upon knowledge
generated by the consensus of a world-wide committee of
experts, we can say that this knowledge is the most
reliable. Then, by decreasingorder of reliability, wefind
the knowledgegenerated by a committee of experts, then
by a group of experts, then by one expert, then by a
person whois not an expert. In a medical domain, the
corresponding types of knowledge are: practice
principles in a monograph,practice guidelines, practice
pathways, practice cases, and medical hokum.
Target case
Interpretation
¯ Certainty: the certainty of knowledgeis associated to the
proof(s) that have permitted to validate it. Wecan
present some knowledge submitted to world-wide
controlled clinical trials as almost certain. Then, by
decreasing order of certainty, we find knowledge
submitted to controlled clinical trials, then uncontrolled
trials, then knowledgegrounded upon an individual’s
experience, and finally on no evidence.
Memorization
Retrieval
This system proposes to assist the different users in the
access to its knowledge-base, depending upon their
authorization level, in the performanceof a set of tasks
adapted to their expertise level. These tasks include
Reactive problem-solving and Predictive problem-solving,
but the Reactive problem-solving task only is presented
here.
In a young medical domain such as BMT, and in a
pioneer center for this therapy such as FHCRC,the
knowledge used for patient problem-solving belongs to
almost all of the types quotedearlier:
1. Practice guidelines: a practice guideline is the
description of a set of rules to follow for a specific
problem-solving situation (mostly a diagnosis or
treatment). Practice guidelines can also be described in
the medical literature. A single practice guideline is
represented by manyrules.
Figure 1. The case-based reasoning cycle.
The general cycle of case-based reasoning is a series of
inferences groupedin steps (Fig. 1). This cycle starts by the
presentation of a target case to solve c,, the form of which
depends upon the cognitive task performed. In the
interpretation step R,, this initial case is transposedfroman
external representation language, that of the application
domain,into an internal representation language, that of the
memorizedcases. Thus this interpretation is a translation
between two representation languages. Moreover, the
indices associated with this case are calculated by an
indexing function, which is often the same as the
inperpretation function. Then, the representation of the case
by its indices is used by the systemin order to comparethis
case with the memorizedcases, during the retrieval step R,.
2. Practice pathways: a practice pathwaycovers the same
type of knowledgeelements as a guideline, but it has
been created by a group of LTFUexperts exclusively for
the computersystem presented here, and by the meansof
a knowledgeacquisition program. It is handled by the
systemas a single rule, that can be quite complex.
3. Practice cases: a practice case is a sampleof a problem-
86
This step is a search through the memoryfor the cases
similar to the new case c,, and leads to a set of extracted
cases. In the reuse step R, a choice is done in favor of the
case(s) whichwill serve as the basis of the reuse in order
propose a processing for the target case. The reasoning R
goes on with a revision step R~, during which the proposed
processing maybe improved. Following, the repaired case
may be added to the memory,and the memoryenriched by
this newexperience, during the memorizationstep R=.
problemis used by the systemin order to match it with the
condition parts of the rules, during the pattern-matching
step Rc. This step is a search throughthe set of rules for the
ones that the problem matches and leads to a set of
applicable rules, called the conflict set. In the conflict
resolution step Rr, a choice is done in favor of the rule
whichwill be fired first. The reasoning RRa
R goes on with a
production step Rp, during which the actions in the action
part of the fired rule are performed. These actions modify
the problem description, by adding new representation
elements, and/or by modifying others. Following, the
elements in the condition part of the rule are removedfrom
the problem description,
while the results of the
performance of the action part are added to the problem
description, and linked to the se of rules, in the working
memory,during the update step R,.
Rcs~ =< Ri, R,, R, R ~, R=>
The memory M of the present system is also its
knowledge-base,and so contains elements, called entities,
different from cases.
The system performs a set of cognitive tasks, such as
diagnosis, planning or interpretation.
Indeed, more
generally, a target case c, is the terms of a problemto solve
by the system:
R~R=< R~, R~, Rr, Rp, R. >
The working memoryW of the production part of the
system contains the updated description of the problem,and
so evolves during the reasoning process. Updatesare done
only in this part of the system, and do not modify the
knowledge-base. This is why this memoryis a working
memory,and contrasts with the CBRmemoryM, which is
a long-term memory.
c, =<S,, St Goal>
with Si being the initial situation, or premises of the
problem, S/ being the final situation of the problem, and
Goal being the result expected from the system (and so
associated with the task t, which is a solution type). For
different task types, a target case may be represented
differently, for instance:
Target
IXffolern
Interpretation
¯ diagnosis: c, = <S, St Goal>with Sf = {} and Goal =
diagnosis;
Update
¯ planning: c, = <S,, Sr Goal>with Sf = target situation and
Goal= plan with the initial state satisfied by Si and the
final state satisfied by.St
A memorized case c, comprises, in addition to the
representationof a target case, a solution Sol.
Rule-Based Reasoning
Certain knowledge entities,
such as pathways and
guidelines, are expressedas rules, and so this systemis also
a RBRsystem. As a RBRsystem, this system performs a
reasoning RRn
R by firing the elements of a set of rules,
called a rule-base in order to perform a set of cognitive
tasks T, for instance a diagnosis or a treatment planning.
Eachrule consists of a condition and an action (if condition
then action).
The general cycle of rule-based reasoning (Fig. 2) is
series of inferences groupedin steps. This cycle starts by
the presentation of a target problemto solve c,, the formof
which depends upon the cognitive task performed. In the
interpretation step Ri, this initial problemis transposed
from an external representation language, that of the
application domain, into an internal representation
language, that of the rules. Then, the representation of the
Figure 2. The rule-based reasoning cycle.
The system performs a set of cognitive tasks, such as
diagnosis, planning or interpretation.
Indeed, more
generally, a target problemc, is the terms of a problemto
solve by the system, and has the samedescription as for the
CBRpart:
c, = <S, St Goal>
with Si, Sj, and Goal being the defined as for CBR(and so
associatedwith the task t, whichis a solution type).
A rule r comprises an action part, which is matched
during RRsR~ with S~ , then with the following Sj
(corresponding to the evolution of the working memory)
87
instances with operators, or relationships betweeninstances
and values:
until SI is satisfied, or Goalif it is empty:
r, = <C,, A,>
with C~being the condition of the rule, or premises, and
A1 being the action of the rule.
Knowledge Representation Language
The system knowledge-base is a network of entities (such
as the practice guidelines, pathways and cases), where
entities are nodes, connectedby links, or relationships. The
links are those defined in the Unified Medical Language
System (UMLS)(Lindberg et al. 1993), that provides
ontology (concepts and links) for the medical domain
general. This ontology has been refined for the application
domain, but the set of 50 links provided between the
concepts was sufficient. The elements of the representation
languageare the following:
¯ A domain ontology, which is the set of class symbols
(also called concepts in the UMLS)
C, where C~ and
denote elements of C, and the set of relationship symbols
(also called relations in the UMLS)
R, where i and R1
denote elements of R. The classes are organized in a
polyhierarchy of classes, and several maincategories can
be described, such as Functions, Diseases, Morphology,
Topographyfor instance. Also, manyclasses describe
Events and Time representation elements
¯ A set of individual symbols (also called instances)
where i and j denote elements of I. Amongthese, some
refer to instances of classes, others to numbers,dates,
and other values. Instances of a class C~are noted aC~
¯ A set of operator symbols O, permits to form logical
expressions composedof classes, instances and other
values, and relationships. Pathways,guidelines and cases
are expressed this way, and such a composition permits
to represent complexentities in a structured way.The set
of operators comprisesthe following:
problem situation = 19 aC~
0 Rj (aCj.,,aCj.2 .... aCj.,)
solution
= 0 aD,
19Rj (aD,.,, aOja.... aDj.~)
with O E O, the default value being v for pathways, and
A for cases and rules. Cases are always expressed using
only A.
Althoughthe representation language is the same for all
the entities, the categories of knowledgeused in problem
situations and in solutions for instance are quite different.
In a medical domainas this one, problemsituation elements
are mostly instances of the Functions hierarchy, mainly
SignsAndSymptomsand Findings. This depends also upon
the task to perform.
There are two main categories of guidelines: diagnosis
guidelines, and treatment guidelines. Guidelines have been
defined by the standard practice committee of FHCRC
and/or LTFU and/or a medical association.
Their
transcription into a form suitable for the system has been
performed by the team members. Pathways have been
defined directly in the system knowledgerepresentation
language,
by LTFU experts through a knowledge
acquisition programdeveloped for this project. Patients
cases have been transcribed from the patients files and
existing database records into a form suitable for the system
by the team domainexperts. For all the entities, a textual
description is provided in addition to the formalized
representation.
The System Reasoning
The reasoning proceeds through different steps (Fig. 3) that
are a merge of the reasoning steps of both case-based
reasoning and rule-based reasoning.
T=rge~
care
A (AND)
v (OR)
=-, (NOT)
ATLEAST n
ATMOST n
EXACTLY n
~, <, <, >, =
Interpretation
MemorizationUpdate
In this representation language, the attributes of a class
are represented via the relationships. A binary relationship,
the arguments of which are an instance and a value mean
that a certain attribute of a class has a certain value, or gets
a certain value.
Guidelines and pathways are expressed as rules
<condition, action>, and cases are expressed as <problem
situation, solution>, wherecondition and problemsituation
have the samerepresentation, and action and solution also
have the same representation, either a composition of
88
cases, pathways,rules
Figure 3. The multimodalreasoning cycle.
4) Reuse(Ru): if the selected entity is a rule, it is fired,
It start with the presentation to the system of a new
problemto solve. This system is capable to handle the wide
variety of problemsthat physicians can face whenthey take
care of patients, and the first task of the system is to
determinethe nature of the problemto solve. This first step,
called the screening step RA, classifies the problemas an
information retrieval task, or a patient problem-solving
task, or yet another task. Onlythe patient problem-solving
task is addressed here. The patient problem-solving task
follows the following steps:
it is a case, its solution is adapted, and if it is a
pathway, it is applied (which is comparable to the
firing of a rule). The applied knowledgeelements may
lead either to a solution proposal for the target
problem, or to the generation of new elements in the
problem description, also called here the working
memory.
5) Update (R): the current case, corresponding to the
working memory,in the process of being solved is
updated, the knowledgerepresentation elements used
by firing a rule are markedas used (they can be used
later for the case-based reasoning). Whatare updated
are the problems dealt with. The solved problems are
removedfrom the description of the problem.
1) Interpretation (R): given the description of a patient
problem, the system constructs, by interpretation, the
initial
situation
expressed in the knowledge
representation language of the system. Abstraction is
the main reasoning type used here, and in particular
temporal abstraction to create trends from timestampeddata (Bichindaritz and Conlon1996). Let c be
the target patient case to solve:
If a solution for the problemanswers all the problems
in the list, then the processing is stopped and the
solution is proposed to the user. Otherwise, the
reasoningcycle restarts at the step Rs.
c¢ = O aC~
0 Rj (aC~.,,aC~.
2 .... aC~,)
2)
6)
Knowledge search (R): the knowledge-base
searched in parallel for applicable rules, pathwaysand
cases. So the pertinent search methodologies,patternmatchingand case-based retrieval are used in parallel.
The result is a conflict set containing both cases,
pathwaysand rules. Let CS be this conflict set: CS = [
c,, rr Pk } wherethe c~ are cases, the rj are rules and the
Pk are pathways.
Memorization(R,): whenthe solution is complete, it
memorizedwith the target case solved.
The output of the system to the user is a user-friendly
formatting of the retrieved entities. Different colors are
used to differentiate betweenguidelines (which are highly
certain since approvedby a large scale committee),practice
pathways(which are less certain because revised by less
experts) and cases (which are less certain because they
represent the expertise of a single expert and have not been
revised at all). The links existing in the knowledge-base
betweenthese retrieved entities are also given to the user.
Moreover,applicable rules are also used to explain results
of the case-based reaosning whenit is used.
3) Conflict resolution (Rr): given the priority given in the
system for the reuse of knowledgebased upon proven
practice, the followinghierarchy of reuse is followed:
I. reuse rules
H. reuse pathways
M. reuse cases.
Discussion
Different methods have been used to achieve the
cooperation of case-based reasoning and other formalisms
of knowledgerepresentation such as rule-based reasoning
or model-based reasoning (MBR).
Twomain categories of such systems can be described:
Nevertheless, the first criteria to choosethe entity to
reuse is the number of problem description elements
matched. The entities are ranked by decreasing number
of matched problem description elements with the
target case to solve, comparing on one hand all the
rules of CS, on another hand all the pathwaysof CS,
and on another hand each case separately.
For equality in this number,the priority order is used,
giving preference to the rules, then the pathways,then
the mostsimilar case.
In the patient examples, the performance of a task
requires all these types of knowledgeto be reused.
Nevertheless, if a current case has several co-occurring
problems, which is generally the case, and if a case
matchesthemall, the case is reused.
89
¯
The systems for which RBRis the main reasoning
process, and CBRis mainly a heuristic to improve the
rule-based reasoning. CABARET
(Rissland and Skalak
1989) resorts to CBRwhen the applicable rules are
contradictory. In a similar way, ANAPRON
(Golding
and Rosenbloom 1991) performs a rule-based
reasoning, but before firing a rule, checks that the
problemto solve is not an exceptionto this rule. In this
case, it resorts to case-basedreasoning.
¯
The systems for which CBRis the main reasoning
process and RBRor MBRare used to take advantage
of a partial domain model available for one part or
another of the reasoning process. Examples of this
cooperation are the ALEXIA
system (Bichindaritz and
Seroussi 1992), where a physio-pathological modelis
used to abstract indices during the interpretation step.
ARCHIE
(Goel et al. 1991) judges the pertinence
cases during the retrieval step by using qualitative
models of the architecture domain. KRITIK(Bhatta
and Goel 1993) uses a qualittive model during the
adpatation step, and CASEY
(Koton 1988) uses rules
to assess the equivalence between features in the
retrieval and the adaptation step.
In the present system, it cannot be said that either the
CBRor the RBR, or another reasoning type, is more
important. Another example of such system is the GREBE
(Branting, 1991), in which RBRis not only used
constrain the CBR.Whenboth methods are applicable, one
is chosenbasedon the degree of certainty of the inferences
performed by one methodor the other.
The present system attempts to perform a closer
cooperation between the methodologies, by separating the
reasoning steps of each of them, and by intertwining them,
so that partial reasoningprocesses and results are obtained
at each reasoningcycle, thus enabling a greater cooperation
between these.
Conclusion
The system presented here is applied to a complexmedical
domain. It is a three years project, and an evaluation is
scheduled for each system componentduring this period.
This evaluation will encompass not only computer
dimensions (such as accuracy, efficiency and userfriendliness) but moreimportantly the impact of the system
on the quality of care, the patients quality of life, the users
satisfaction and the cost of care.
Acknowledgments
This work is supported in part by grant R01HS09407
from
the AHCPR
(Agency on Health Care Policy and Research).
References
Bhatta, S.R., and Goel, A.K. 1993. Model-BasedLearning
of Structural Indices to DesignCases. In Proceedingsof the
Workshop of IJCAI-93,
Reuse of Designs:
an
interdisciplinary cognitive approach, A1-A13.Chambery,
France.
Bichindaritz, I., and Seroussi, B. 1992. Contraindre
ranalogie par la causalit6, Techniques et Science
Informatiques Volume11, n 4: 69-98.
Bichindaritz, I. 1994. Apprentissage de concepts dans une
m6moire dynamique: raisonnement ~t partir de cas
adaptable ~ la t,~che cognitive. Ph.D. diss., Universit6 Ren6
Descartes.
Bichindaritz, I. 1994. A case-based assistant for clinical
9O
psychiatry expertise. In Proceedings of SCAMC-94,
673677. AMIA,Washington D.C.
Bichindaritz, I. 1995. A memorymodelfor an intelligent
system for the 21st century. In Proceedings IEEE
Conference on Systems, Manand Cybernetics 2233-2238.
Bichindaritz, I. 1995. Incremental Concept Learning and
Case-based Reasoning: for a Co-operative Approach. In
Progress in Case-basedReasoning,Watson,I. edt., 91-106.
Springer-Verlag LNCS/LNAI
1020.
Bichindaritz, I., and Conlon, E. 1996. Temporalknowledge
representation and organization for case-based reasoning.
In Proceedings TIME-96,24-29. IEEE Society Press.
Bichindaritz, I. 1996. MNAOMIA:
Improving Case-Based
Reasoning for an Application in Psychiatry. In AAAI
Spring Symposiumon Artificial Intelligence in Medicine,
14-20. Stanford, Calif.: AAAIPress.
Branting, K.L. 1991. Exploiting the Complementarity of
Rules and Presedents with Reciprocity and Fairness. In
Proceedings of a Workshop on case-based reasoning
(DARPA),130-132. San Mateo, Calif.: MorganKaufmann.
Goel, A.K., Kolodner, J.L., Pearce, M., Billington, R. and
Zimring, C. 1991. Towards a Case-Based Tool for Aiding
Conceptual Design Problem Solving. In Proceedings of a
Workshop on case-based reasoning (DARPA), 109-120.
San Mateo, Calif.: MorganKaufmann.
Koton, P. 1988. Reasoning about evidence in causal
explanations. In Proceedings of AAAI-88,256-261. San
Mateo, Calif.: MorganKaufmann.
Lindberg, D.A.B., Humphreys, B.L., and McCray, A.L.
1993. The Unified Medical Language System. Meth Inform
Med32.
Rissland, E.L., and Skalak, D.B. 1989. CombiningCaseBased and Rule-Based Reasoning : A Heuristic Approach.
In Proceedings of IJCAI-89, 524-530. San Mateo, Calif.:
Morgan Kaufmann.