RENOIR: An expert system using fuzzy logic for rheumatology diagnosis

RENOIR: An Expert System Using Fuzzy Logic for Rheumatology Diagnosis Miguel Belmonte-Serrano Seccidn de Reumatologia, Hospital General de Castelfdn, 72004, Castellon, Spain; and Centre d’Estudis Avanqats de Blanes, Girona (CSIC), Spain

Carlos Sierra and Ramon Lopez de Mantaras Centre d’Estudis Avanqats de Blanes, Girona (CSIC), Spain

A new expert system (ES) to aid the nonspecialist physician in diagnosing arthritis and collagen diseases has been developed. Here we present the structure of RENOIR and the results of its implementation. This rule-based ES has been programmed using the MILORD environment. This is a shell to develop ES using a closed set of linguistic labels to express uncertainty. A feature of RENOIR is its five levels of knowledge representation, which permits to build a very flexible knowledge base (KB) and express knowledge with high accuracy. Those rules directed to similar goals are grouped in modules to improve computational performance and for higher clarity of the KB. Control of the reasoning process is assured by several mechanisms, one of the main being metarules specifically designed for almost all the knowledge levels of the KB. We have used public domain knowledge (books, criteria tables) and personal heuristics from one of the authors (Belmonte-Serrano) to implement the KB of RENOIR. In its present form, our KB comprises 1 058 rules, 978 facts, 220 metarules, and 34 modules. A first validation process has shown good performance of the ES compared to 12 physicians with diverse levels of experience in rheumatic diseases. New ongoing versions of the system with improved interfaces,and reasoning capabilities are expected before verifying RENOIR’s clinical acceptability. 0 1994 John Wiley & Sons, Inc.

I. INTRODUCTION

There is an overall agreement that diagnosis is a process necessary to achieve right therapeutic decisions in medicine. Diagnosing rheumatic diseases is usually difficult to the nonspecialist physician, partly due to lack of skills through the pregraduate studies, and partly because of the absence of pathognomonic findings in many rheumatic diseases. If we look at most classification criteria tables we see that diagnosis (which is in itself basically a classification task’ is usually performed combining a bizarre group of findings. For the same reasons, the nosology of the rheumatic diseases is not clear-cut. In this sense, we can say that rheumatology is a “fuzzy” domain of knowledge, where fuzzy means that the limits or boundaries among the diseases are not always well defined and that the information there managed has high levels of uncertainty

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, VOL. 9, 985-1000 (1994) 0 1994 John Wiley & Sons, Inc. CCC 0884-8173/94/110985-16

986 BELMONTE-SERRANO, SIERRA, AND DE MANTARAS

associated. Clear examples of these statements are collagen diseases and vasculitis.

Expert systems (ES) are computer programs that try to replicate some aspect of the human intelligence, namely speech recognition, artificial vision, logical reasoning, and learning from experience.' Since, over two decades of efforts to develop ES in the medical domain have been produced. MYCIN was a pioneer program implemented in 1976 to give advice on infectious diseases.2 Only a few systems have been developed in rheumatology successfully enough to go through a validation process. The best known in the rheumatologic domain are CADIAG, developed in Vienna by Adlassnig et al.3-5 and AIlRHEUM from Kingsland and Lindberg et al: , of the Missouri and Rutgers Universities.6 Another successful A1 system, INTERNIST-I/QMR,7 was designed to deal with internal medicine problems and also includes several rheumatologic diagnoses.

These systems are quite different in the span of diseases included and the paradigms of knowledge representation (KR) they use. INTERNIST-I is a frame-based system and works in a quite similar way to that of CADIAG-2;7 however, the inference engine of the former is based on heuristics and ranking of scores of symptom-disease relationships, while the later is based on fuzzy logic mechanisms. Also, CADIAG-2 knowledge base (KB) is focused mainly on rheumatic and gastroenterological diseases, currently covering about 185 and 110 diseases, re~pectively.~ A first global validation of CADIAG-2 showed good accuracy for some rheumatic diseases but not for other^,^ although a more recent evaluation on 154 patients with rheumatoid arthritis gave an accuracy rate of 88.7%.5 AURHEUM is a rule-based ES which uses the inference engine EXPERT* to state a causal-inferential network. Its KB was created mainly introducing criteria tables for 26 common rheumatic diseases. This system has performed well in several validation processes, including complex overlap syndromes in colagen disease^.^^'^

A recent article describes yet a new ES on rheumatology developed in Germany." It covers 32 diseases with only 60 questions about medical history. Its performance in an open validation trial seemed not very satisfactory when compared with other systems as misinterpretation of computer diagnoses oc- curred in 25% of cases.

In this article we present RENOIR, a new expert system based in fuzzy set theory aimed to aid the nonspecialist physician to diagnose rheumatic diseases. Its knowledge base spans over the 37 major diagnoses in collagen diseases and inflammatory arthropathies and 15 diagnostic variants. These are listed in Table I. Our system has been validated in a multicentric trial that has been described in another article.'*

11. METHODS

We briefly describe our programming environment (MILORD) first from the standpoint of knowledge engineering, then the management of uncertainty, the knowledge representation structure, and finally the control mechanisms.

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Table I. List of diagnosis known to the RENOIR system. Inflammatory Idiopathic Arthritis

Rheumatoid arthritis Palindromic rheumatism Intermittent hydrarthrosis Adult Still disease

Childhood Onset Arthritis Juvenile chronic arthritis (systemic, oligoarticular an polyarticular onset) Rheumatic fever

Crystal Induced Arthritis Uric Gout CPPD

Spondylarthropathies Ankylosing Spondylitis Reitedreactive arthritis Psoriatic arthritis (5 subtypes) Crohn & Ulcerative Colitis related arthropathy Whipple disease

Collagen Diseases Systemic lupus erythematosus Dermato/polymyositis Sjogren disease Systemic progressive sclerosis CREST syndrome Mixed connective tissue disease Anticardiolipin syndrome

Vasculitides Polymyalgia rheumatica Giant cell/temporal arteritis BehGet disease

Infectious Osteoarticular Diseases Bacterial, nongonococcal arthritis Gonococcal arthritis Tuberculous arthritis Brucellar arthritis Bacterial infectious spondylitis Pott’s disease Brucellar spondylitis Viral arthritis (four types)

Primary articular osteoarthritis Vertebral osteoarthritis (spondylosis) Fibromyalgia Other myopathies (Steinert disease, Trichinosis)

Miscellaneous


Tab12 11. Some examples of facts in RENOIR. Type Example Valueh

Numeric GLYCEMIA 30 Subset AUTO-AB {FANA+ DNA+ RF+ ACA+ SSA+ RNP+} Boolean LAB-RESULTS [YES NO] FUZZY HEPATOMEGALY {IMPOSS ALM-IMPOSS. . . POSS. . . SURE}

A. Knowledge Engineering

We shall not explain in depth the knowledge engineering basis underlying the RENOIR system, it has been developed using the MILORD programming environment . l 3 9 I 4 MILORD itself was developed using COMMON LISP, the details about MILORD can be found in a previous publication in this journal.14 Here we give just a basic description of MILORD. The inference engine of MILORD is directed to interpret production rules of the form:

IF {condition} THEN {action}.

The condition can have one or several premises (combined by AND opera- tors) such as [skin problem is a butterfly facial rash], [swollen joints are (wrist, elbow, knee)]. The most usual action of a rule is to conclude other facts giving to them an associated certainty value. In our case, the solution is a deducible fact containing the patient’s diagnosis.

The medical knowledge included in the KB comes from three main sources:

(a) widely available rheumatologic concepts from books, manuals, and journals; (b) the more recent and internationally accepted classification criteria tables and

(c) rules-of-thumb extracted from the clinical experience of one of the authors trees for those diseases where such criteria are available; and

(Belmonte-Serrano).

B. Management of Uncertainty

The basic elements of the KB are calledfucts. These elements are used to represent the most elementary concepts of the domain. There are four basic types of facts, depending on the type of value that they can have associated: numeric (e.g., glycemia), subset (used to form menus of choices), boolean (with only two values accepted: truelfalse), and fuzzy. Some examples are shown in Table 11.

Fuzzy facts are those which cannot be stated with total security and that are nonnumeric, as e.g., {hepatomegaly} or {arthritis}, i.e., we use the term “fuzzy” to refer to uncertain facts only, i.e., we do not deal with imprecision. These facts have associated a linguistic certainty value represented by a fuzzy interval chosen among a set of nine ordered values: impossible, almost impossible, slightly possible, moderately possible, possible, quite possible, very possible, and sure.

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Premises can be constructed using a single fact, the negation of a fact, and a value or range of values (in subset-type of facts). Premises with numeric facts can be algebraic comparisons (e.g., glycemia > 120) or numerical calculus (e.g., [eos-abs {WBC * eos%}]).* The rules always have a linguistic certainty value. Certainty values are propagated through and combined in the rules during the reasoning process by applying the rules of the algebra of fuzzy intervals, according to the fuzzy set theory of Zadeh.” This issue escapes to the purpose of this article and has been explained in detail e1~ewhere.l~

C. Structure of Represented Knowledge

Knowledge in RENOIR is hierarchically structured in five representation

(1) In the first level of knowledge we have thefacts, which are the basic elements or objects used to conceptualize the domain to be represented and to build all the other parts of the system.

(2 ) The associative level consists in the production rules used to deduce new information, forming thus an associative knowledge net. On the other hand, facts can establish hierarchical relations among them. These relational links are then used to limit the search space during interactive consultations.

(3) These rules can be grouped whenever they are related to the same goal. These groups, called modules, are especially useful to improve both the efficiency and the understanding of the KB for an outside observer. As modules divide the KB in smaller, handier areas of knowledge, they facilitate the construction and debugging tasks to the KB developer. Even more important is the fact that they are the basic element that the inference engine (IE) uses during the process of consulting. The set of modules form the structural level of RENOIR.

(4) A list of modules that the system will visit sequentially is called a strategy. It represents a hypothesis that the system generates about how to solve the actual problem. This is the basic structure used to steer the reasoning process. Strate- gies are dynamically produced by special rules (called metarules, see below) during the consulting process. Strategies form the hypothetical Knowledge Rep- resentation (KR) level in RENOIR

(5) Only one problem-solving strategy can be active at any time. As every metarule may generate a new strategy, these must be combined to form a single structure called action plan. A plan is a list of modules to visit and modules to inhibit. It also keeps track of the metarules and facts used to build it. Strategies and plans have fuzzy values associated with them; these values are combined by the IE through the consultation to focus the reasoning process. Plans from the fifth and last level of KR, called the heuristic level.

These concepts will be revisited in the results section.

levels provided by MILORD. These will be briefly described as follows:

i

D. Control Mechanisms for the Reasoning Process

MILORD allows to set up a variety of control mechanisms to steer the reasoning process of a consultation with high flexibility. The main control mechanism is by using a special type of rules to determine in every moment

*Which means: Absolute number of eosinophiles/mm3 = (total leukocytes/mm3 * ratio of eosinophiles).


Table 111. Some examples of rule and metarule. Rule Metaruie

Name ID R23002 MRO 128

Condition IF LOSS-STRENGTH SEROSITIS (premises) LAB-RESULTS

CPK >= 3 ( x normal)

Action THEN CONCLUDE MYOPATHY

VISIT-MOD ULE M-SLE & M-MCTD

Rule certaintv QUITE POSSIBLE POSSIBLE

which rules are applicable and which are not. This is done depending on the specific context of the consult currently performed. These special rules express a higher level knowledge over the domain and therefore they are named metarules. Their structure is similar to plain rules applying to domain objects (facts), but the “action” part differs in that it is not oriented to deduce other facts but to control the inference engine. An example of each one is shown Table 111.

There are several types of metarules, depending on the type of action undertaken. This in turn is related to the level of KR to which they apply. So, depending on the context of the problem they can activate or delete facts, rules or modules. They are also used to build strategies and plans.

111. RESULTS

The knowledge base of RENOIR spans along 37 main inflammatory arthropathies and collagen diseases plus 15 diagnostic subtypes of these. Table I shows the list of diagnoses of the KB. The number of elements in RENOIR’s knowledge base are summarized in Table IV.

Although our goal was not to diagnose noninflammatory diseases, we de- cided to implement a small group of rules for osteoarthritis, fibromyalgia, and noninflammatory myopathies (Steinert disease, trichinosis) for validation purposes to test the accuracy of RENOIR against such diagnoses. This way, we expect to expand the KB in the future to cover most rheumatic diseases.

MILORD allows for the implementation of very flexible knowledge bases, As previously stated, in RENOIR the knowledge is structured according to the

Table IV. Contents of RENOIR’s knowledge base.

978 Facts 1 058 Production rules

34 Modules 26 Rule-oriented metarules

194 Strategv-oriented metarules

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Table V. Level Domain Control

Heuristic Plans mr-plans Hypothetical Strategies mr-strategy Structural Modules search/threshold Associative & relational Rules & associative graph mr-rules

mr-graph Universe of discourse Facts

Multilevel architecture of RENOIR’s knowledge base.

five different levels provided by MILORD: universe of discourse, associative and relational, structural, hypothetical, and heuristic. We shall describe our results following this schema, which is shown in Table V.

A. Universe of Discourse: The Facts

Our KB contains 978 individual basic objects, the facts . These are the elements used to describe the most elementary instances of the domain here represented. As described in Sec. 11, they can be of any of four generic types: fuzzy, boolean, subset, and numeric. Values for these facts are introduced by the user when solicited by the system during a consultation, or in other cases they are deduced from other facts through associative rules. The first type are called nondeducible facts and have a question associated. They cannot ever be found in the conclusion of any rule of the KB. The other type are called deducible facts and they are always found in the conclusion of at least one rule. Deducible facts will never be asked to the user. One specific fact always belongs to one and only one of these classes. RENOIR has been developed thinking of a user with an average level of medical knowledge, so the questions assume a minimal experience with clinical exploration and history-taking proce- dures.

Our system is based on monotonic reasoning, which means that once a fact has given a value it cannot be modified during the same consultation. It is possible to perform further consultations accumulating new and older information days or weeks later, but previously given values cannot be changed. As most of other ES, our system is not designed to effectively manage time-oriented data except when given as a simple statement.

An important feature of facts is that they can have associated a label to set hierarchic relationships between them. For instance, the fact “DE- PRESSED” is related to the fact “PSYCHIATRY” by the relational label “belongs-to” composing a triplet of the form “DEPRESSED belongs-to PSY- CHIATRY”. Other relational labels herein used are: type-of, subset-of, crite- rion-of, and class.

This explicit relations between facts form an oriented relational graph that can be used to restrict the search space during consultations. Moreover, this feature is used to classify the facts in homogeneous groups, thus facilitating reading and debugging the KB. The main groups of facts in RENOIR are:


anamnesis, exploration, hematology, biochemistry, serology, urine, microbiol- ogy, skin, spine, articular, synovial-liquid, diagnosis, therapy, and miscellaneous.

B. Relational and Associative Level: Fact-Oriented Rules

There are specific metarules to deal with the relational graph during a consultation. When the available information shows evidence that a main element of a class is false then it is possible to eliminate all the facts hierarchically depending on that fact. This process amounts to pruning the relational graph. For instance, if BIOCHEMISTRY is false this means that we don’t yet have biochemistry results. Thus, all the facts related to this procedure are not useful to the current reasoning process and thereof they are marked as deleted. This method greatly improves the computational efficiency of the system.

Production rules play a central role for knowledge representation in RENOIR. The underlying assumption is that linking concepts or facts to one another will lead to relevant conclusions. This deductive process is implemented in RENOIR through 1058 production rules which compose the associative knowledge level of the system. The applicability of production rules is controlled through 26 rule-oriented metarules and also, indirectly, by pruning metarules that act on the facts of their condition.

C. Structural Level: The Modules

Production rules directly or indirectly related to the same goal are grouped in our system forming 34 modules. These are the main working units for the reasoning process and are shown in Table VI.

Modularity has the following advantages:

(a) Allows to build and follow a line of reasoning by means of a list of modules to

(b) Makes construction and updating of the KB easier (c) Improves the computational efficiency of the system (d) The system structure is easier to understand for an observer not involved in

be visited through the consultation process

the development project

The current version of RENOIR runs with nonstructured modules. This has the following drawbacks:

(a) As in many other similar systems, the knowledge included in one module is hidden to the rest of the KB before that module has been evaluated

(b) When similar goals are included in different modules there is risk of duplicity of some rules. This is especially true when, as in our system, a taxonomy with overlapping facts is used as a backbone to organize the modules.

These problems will be solved in the future using structured modules as they

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MR

01 02

03

04 05

06

06B 07 08

09 10

1 1

12 13 14 15 16 17 18 19 20 21 22 23

24 25 26 27 28 29 30 31 32

Table VI. Modules and goals of the knowledge base of RENOIR. Num Name Rules Goals in the module

MR-INICIAL 194 NIL (metam6dulo)

MENU C ARACT- ARTIC

LIQUID ARTIC

RADIOLOGIA-COL HEMOGRAMA

BIOQUIMICA

M-SEROLOGIES M-ORINA CARACT-RAQUIS

RAQUIS-INFL CAR - P E L L

M-INFECCIO

M-ARTR-REUM M-FEBRE-REUM M-LES M-PALINDROMIC M-STILL M-ESPOND-ANQUIL M-REITER M-PSOR M-ENTEROP M-GOTA M-CONDRO M-MIOPATIA

M-ESP M-EMTC M-PMR M-CRON-JUV M-SJOGREN M-BEHCET M-VIRAL M-FIBROSIT M-ARTROSI

74 89

40

4 31

43

22 8

37

21 39

65

39 26 49 28 18 46 49 25 37 31 14 28

42 26 32 15 31 21 7 9

12

FITXA MONOARTR-AGUDA PATRO-AR -GOTA -SERONEG ARTROSI . . . LIQ-I LIQ-I1 HEMARTROS MICROCRI- STALLS ARTR-SEPT . . . ESPOND-INFL ESPOND-NOINFL

TROMBOPENIA ALT-COAG . . . NEUTROPENIA LEUCOCITOSI

DIABETIS MIOSITIS HCA CBP HIPERURICEMIA INSUFRENAL . . NEFROPATIA NEFRITIS NEFROSI COLAGEN F-R REACTIVA BRUCELOSI

SACROILEITIS-INF ESPOND-INF ESPONDARTR ESPONDILOSI ESPONDILITIS POTT ESPOND-BRUC PSORIASI M-R-PELL VASCULITIS COLAGEN AFTES-BEHCET . . ARTR-SEPT ARTR-ESTAFILO ARTR-TBC ARTR-BRUCELLA. . . ARTR-REUM FELTY CAPLAN FEBRE-REUM LUPUS SDE-CARDIOLIP PALINDROMIC HIDRARTR-INTERM STILL ESP-ANQ ARTR-GONO REACTIVA REITER ARTR-PSOR -MUTIL -0LIGO -DIST -AR -EA CROHN COLULC WHIPPLE GOTA CONDRO CONDRO-SEC DMPM STEINTERT TRIQUINOSIS MIOP- HIPERTIFUHIPOTIR ESP CREST EMTC PMR HORTON

SJOGREN BEHCET

FIBROMIALGIA

ACJ ACJ-OLIGO ACJ-POL1

ARTR-RUBEOLA ARTR-HEPAT

POLIARTROSI ARTROSI-CADERA ARTROSI-GENOLL. . .

permit a more effective control of the input and output of knowledge in the modules.

Modules in RENOIR can be grouped into the five general classes shown in Table VII:

(a) A data-gathering module named menu. Most of the facts in the rules of this module are of the subset type, so at the beginning of the consultation the data are mainly gathered in a menu-driven fashion. This method narrows quickly


Table VII. Functional classification of modules in RENOIR. Data Gathering module 01

Intermediate Hypotheses Generating (IHG) modules 02 08 10

Data Abstracting modules 04 05 06 06B 07 03

Diagnostic-seeking modules 09 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

MENU

CARACT- ARTIC CARACT-RAQUIS CAR-PELL

RADIOLOGIA-COL HEMOGRAMA BIOQUIMICA M-SEROLOGIES M-ORINA LIQUID-ARTIC

RAQUIS-INFL M-INFECCIO M-ARTR-REUM M-FEBRE-REUM M-LES M-PALINDROMIC M-STILL M-ESPOND-ANQUIL M-REITER M-PSOR M-ENTEROP M-GOTA M-CONDRO M-MIOPATIA M-ESP M-EMTC M-PMR M-CRON-JUV M-S JOGREN M-BEHCET M-VIRAL M-FIBROSIT

32 M-ARTROSI

and efficiently the search space of the problem in the very first steps of the consultation,

(b) Four intermediate hypothesis-generating (IHG) modules intended to develop intermediate hypothesis such as “chronic polyarthritis” or “serositis”. This process is done by refining the basic data gathered with the first module. These intermedite hypothesis are used to set the problem in more concrete search spaces or “contexts”.

(c) Twenty-three disease-specific modules to refine the reasoning process. Their goals are single disease entities as “rheumatoid arthritis” or “polymyositis”,

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(d) Five general purpose modules to abstract concepts from laboratory and radio-

(e) One last module with 194 metarules to generate strategies (see 1II.D) and prune logic data,

the relational graph when applicable.

The order in which rules are written has generally little importance to guide the reasoning of the system. In fact, the priority for evaluating the rules inside any module is determined by the following criteria:

(1) Type of search: backward or forward-When we apply forward reasoning we evaluate all the rules with facts already proved true in their premises. So we get all possible goals achievable with the current available information. With backward reasoning we first select one goal and evaluate those rules more relevant to prove the goal.

(2 ) Level of certainty-Those rules with higher certainty values associated are evaluated before others with lower certainty.

(3) Subsumption-The most specific rules will be evaluated first. If they succeed then the IE will skip more general rules that otherwise will be evaluated. We say that a rule is more specific than (or that it is subsumed by) another when the set of premises in its condition is included in the set of premises in another rule’s condition. Both must conclude the same fact, though with different associated certainty value.

(4) The natural order in which .rules are written in the module.

D. Hypothetical Level: The Strategies

In order to achieve credibility for the general user of the system who is undertaking a consultation, it is essential that the system poses its questions and gives answers in logical sequence, as should be expected from an expert physician. So, the order and the number of the modules to be evaluated is one of the most important subjects to be considered while developing an ES. In our system we call strategy to a ranked list of modules to be sequentially visited by the IE. This represents a solving hypothesis to achieve the solution of the problem.

There are 194 metarules in RENOIR directed to dynamically generate these strategies. During the consultation any new information acquired by the system, be it deduced or externally introduced, is checked by a forward reasoning engine against the condition of the metarules of the KB. If any of these succeeds then the action part of that metarule is executed. Namely, there are two types of action for these metarules:

(a) VISIT-MODULES (list-of-modules). It inserts a list of modules to visit as a

(b) ELIMINATE-MODULES {list-of-modules). The modules in that list are new strategy.

marked as not applicable for that specific consultation.

The object of this second type of metarule is to avoid inconsistencies in the reasoning process and to improve the efficiency of the system. For instance,


if we know that the patient is 78 years old, then we can exclude all modules referring to diseases developed in young people.

E. Heuristic Level: The Plans

It is clear that only one line of reasoning can guide the inferential process of the system in every moment. Usually more than one strategy metarule will be activated during a consultation so that there can be more than one strategy generated in a given moment. To deal with this problem there are some special metarules embedded in the IE to combine strategies and build a unique structure to effectively guide the reasoning of the system. This unique structure, called the action plan of reasoning (or simply, the plan) , is a list of modules to visit, modules eliminated, and facts and metarules which support that specific plan.

Every time a new strategy metarule is activated, its resulting strategy is combined with the actual plan of the system. Then, depending on the certainty value of the metarule and the plan, the lists of modules to visit suggested by the metarule and the plan are combined. If some of the modules in the metarule’s action were already in the plan’s list of modules to visit, they advance to the top of the list.

The way plans and the newly formed strategies are combined depends on the following general, situation-specific heuristic principles:

(a) The Ockham razor, which states that the more probable solution to a problem is the one that explains most of the current findings in this problem. This is the heuristic most frequently used when solving a case with RENOIR.

(b) When one strategy or plan is a subset of another in the solutions space, then we choose the more specific one.

(c) We say that two plans or strategies are incompatible when one is trying to eliminate the modules listed to visit in the other. Then we choose the one with a higher level of certainty associated.

These heuristic principles are domain independent, so they are included as heuristic rules in the IE which are transparent to both the user and the KB developer.

F. Finishing the Diagnostic Process

The diagnostic process ends when any of the following situations is achieved :

(a) There are no more modules to evaluate in the VISIT list of the plan, (b) The certainty level for a given diagnosis is higher than a predefined threshold. (c) STOP. When the information gathered shows evidence that the case does not

belong to the KB’s domain, RENOIR uses a metarule with this action to stop the reasoning process.

(d) CONCLUDE-DIAG (diagnostic-fact). When pathognomonic information is available to the system, this type of metarule is used to stop the reasoning process and give the final diagnosis immediately.

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It should be noted that RENOIR has not been primarily designed to deal with multiple rheumatic diagnoses in the same patient.

G. Cycle of the Reasoning Process

During any consultation, when the user is prompted for a new finding, he can invoke a submenu to ask why a fact is solicited and/or to know what rules have been used to support a deduced fact. This is used also as a method for explaining the system’s conclusions. The KB developer can use a three-level trace system to see interactively the reasoning process while a consultation is in progress. With this debugging facility the KB developer can detect errors and see where loops or inconsistencies are produced.

At the end of the process it is possible to automatically generate several types of reports. One is directed to validation purposes and the other is to keep track of the facts, rules, metarules, and strategies used during the consultation process.

H. Performance and Evaluation

The performance of RENOIR at this moment seems acceptable, though it has not been tested in a clinical environment.

We have, however, performed a preliminary validation of RENOIR based on 32 “paper patients” from unselected hospitalization reports of patients in our hospital. In this test, RENOIR reached an overall 75% diagnostic accuracy when comparing the system’s diagnoses with those in patient charts and reports. A formal, double blind, multicentric validation of 81 cases from 5 hospitals in several communities of Spain has been developed afterwards. In this validation fully described elsewhere,’* a cluster analysis of the results was made to com- pare the way our system and 12 physicians with diverse experience in rheumatology performed diagnostic tasks. RENOIR clustered near enough to the main experts’ cluster, so we can say that its performance is indeed acceptable. The cases were selected in a pseudo-random manner so that all of them belong to the domain known to our system. If future validations confirm these encouraging results then an open validation will be undertaken to test the acceptance of the system in a clinical setting.

Each consultation takes 5 to 15 min to run in a VAX station. Macintosh and IBM-PC versions are currently under active development.

IV. FINAL REMARKS

Rheumatology is a medical speciality in which facts, nosology, and diagnostic criteria are characterized by high levels of uncertainty associated. Thus, it is a good subject for developing expert systems to aid in diagnosing and therapy. To date only a few ES have been successfully developed in rheumatology. We have here presented a new rule-based ES to aid the nonspecialist physician in diagnosing arthritis and collagen diseases.


The size of our KB (over 1 000 rules) is comparable to other rule-based systems.8 Some features in RENOIR are particular to our system. The multilevel knowledge representation in MILORD allows for writing concepts accurately and thus it has proved to be a flexible and powerful tool for developing our KB.

RENOIR uses specific control systems in the four top levels of KR to steer the reasoning process. In the main, they assume the form of classical metarules’6 with actions directed to any of the levels of the KB. Specific thresholds for the diagnostic of each disease in the KB are settled in a separate control module. The top level heuristics for general problem solving are implemented in the form of metarules. As they are nonspecific to the domain, these metarules are embedded in the IE and become transparent to the user and the KB developer.

Uncertainty is modeled in RENOIR by fuzzy intervals representing the meaning of linguistic certainty values, following fuzzy set theory. l5 These labels are a closed set of choices for the user to express his confidence in the data he is introducing to the system when asked for new findings. Also, linguistic values are used by the KB developer to give certainty weights to the rules, facts, and thresholds. Most rule-based ES deal with uncertainty by using confidence factors (CF), in the form of a single number chosen within a predefined range.6g16 However, this approach has some theoretical disadvantages. It has been argued that numeric human estimation of uncertainty has little interobserver and in- traobserver consistency. Even worse, most physicians are unable to make a fair estimation of the inaccuracy of theirjudgments, making far larger estimation errors than the boundaries accepted by themselves as fea~ib1e.I~ On the other hand, uncertainty judgments in the form of fuzzy intervais seem to have higher consistency in this case. Recent psychological studies have shown the feasibility of fuzzy interval-based scales in the form of linguistic labels to represent uncertainty.** In consequence, our system was developed by using this type of uncertainty representation.

There are some problems yet to be solved in our system. Many of these are also present in other ES developed up to date. RENOIR has not been designed to diagnose multiple diseases in a single patient, though we have been quite successful in trying to make simple links between related diseases such as Sjogren disease and RA or SLE. It’s difficult to manage facts that are variable with time or that include time relationships, such as chronidacute or recurrent/ steady manifestations. The user and KB developer interface is rather poor and there is no pile editor nor graphical display. We used nonstructured modules, which means that knowledge in one module is not inaccessible to other parts of the KB before it is evaluated. This can make the implementation and control of the reasoning process difficult, sometimes leading to duplication of rules in different modules to assure they won’t be missed. Also, nonstructured are evaluated as a whole unit. In this sense, this implies the need to analyze all the module’s goals even when only a part of the information that it contains is required.

A rule editor and tools for off-line checking the internal consistency of the KB are in progress. A new version of MILORD with improved interface and

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editing capabilities is ongoing. Improvements of the IE will permit structured modularity so input and output from and of the modules will be strictly controlled. This new version is based on MILORD-11, a modular language that has been developed based on our experience with MILORD.

In summary, we have presented the structure and results of a new expert system in the domain of the collagen diseases and arthritis. We make special emphasis in the uncertainty management with linguistic labels and the multilevel representation and control structures of RENOIR. This is an experimental system intended to be used as a diagnostic aid for nonrheumatologist physicians and also for medical training in this speciality. A first validation has demon- strated the good performance of the system, though some of its features should be improved before testing it in clinical settings.

Supported in part by FISS Grant 8910074 from Fondo de Investigaciones Sanitarias de la Seguridad Social (INSALUD-Spain).

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RENOIR: An expert system using fuzzy logic for rheumatology diagnosis

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