Case-based reasoning for AIDS initial assessment

Case-based reasoning for AIDS initial assessment Li D Xu

Case-based reasoning solves a problem by retrieving from its case memory a solution which has solved a similar problem in the past, and then adapting the solution to the problem. Recent work in knowledge-based systems in AIDS initial assessment (AIA) reflects a growing interest in the case-based paradigm. The reason is that AIA experts rely heavily on memory of previous cases when assessing AIDS-risky behaviors. The paper describes a case-based system that uses experience in the form of previous cases to assess AIDS-risky behaviors. An important feature of the system is that fuzzy mathematical algorithms are used to retrieve previous cases and determine the best matching case. The paper concludes with a discussion of future research. Keywords: case-based reasoning, fuzzy set theory, health care

Acquired immunodeficiency syndrome (AIDS) has been called the most serious public health problem worldwide today. AIDS is believed to be caused by the human immunodeficiency virus (HIV). In response to the epidemic, a variety of intervention and prevention measures have been instituted. Instead of serving those who are HIV positive, the purpose of AIDS intervention and prevention (AIP) is to target those who have high risk behaviors such as unsafe sex, drug injection, and needle sharing. In an AIP program, one of the major routine operations is AIDS initial assessment (AIA)L The purpose of AIA is to identify those who are at risk and could be targeted for AIDS intervention. AIA is also called the screening of AIDS-risky behaviors. In AIA, AIP specialists extract the data from the database, interpret

Department of MSIS, Wright State University, Dayton, OH 45435, USA Paper received 16 August 1993. Revised paper received 17 February 1994. Accepted 25 March 1994.

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the data and determine who is eligible to participate in the AIP project. The assessment is based on the drug and sex behavior of individuals, the prevalence of HIV infection, and a number of other risk factors. To be eligible to participate in the AIP project, an individual must have (a) injected drugs intravenously during the last six months, (b) been out of drug treatment programs for at least 30 days, (c) had a sexual partner(s) who injected drugs intravenously during the last six months and who is/are not in drug treatment programs, and/or (d) had an HIV antibody blood test (an enzyme-linked immunosorbent assay (Elisa test) ~ 5. Only if the first Elisa test is positive is a second Elisa test performed. If that test is also positive, the confirmatory Western blot test will then be performed. An individual is considered positive for antibodies to HIV when all three tests are consistently positive 2. Other factors that are considered in the screening include demographic background, age of drug injection initiation, drug treatment episodes, incarceration history, and fewer opportunities to occupy the conventional roles of worker, spouse and mother etc. 6. An AIP specialist uses a combination of the information to determine whether an individual is at risk and eligible to participate in the AIP project. Recently, such information has been compiled into rules for rule-based knowledge systems 7. Case-based reasoning (CBR) is the problem-solving paradigm where past experiences are used to guide problem solving 8. In CBR, cases similar to the current problem are retrieved from the case memory, and the best match is selected from those retrieved and adapted to fit the current problem based on the differences between the previous and current cases. In AIA, a large set of screening knowledge is episodic. AIP specialists often screen AIDS-risky behaviors by relating the current subject to the previous screening cases, i.e., rather than creating an assessment from scratch, AIP specialists recall those cases similar to the new case and interpret the new case by reasoning with past interpretations. Sometimes previous cases address only part of the new case, and, in this case, AIP specialists may

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Case-based reasoning for AIDS initial assessment: L D Xu

apply a modified version of the previous case to the new case. As can be seen, the CBR approach is appropriate for the AIA domain; previous cases are applied to the new case, and previous cases may be modified to address the new case for which those previous cases are insufficient for reasoning. Although AIA is a domain amenable to CBR, some challenging issues are involved. First is the issue of retrieving previous cases. For example, while trying to retrieve previous cases, vague and incomplete information may be involved9. To support such retrieval, it is necessary to develop fuzzy retrieval strategies. Second, the best matches often meet a set of criteria ~°. In general, the best matches that meet the criteria can be successfully used. The challenge is to develop fuzzy multiobjective selection algorithms. This paper describes a prototype case-based system for AIA. The rest of the paper is organized as follows. The second section discusses the system architecture, the main components of the system, and the fuzzy mathematical algorithms which are used to retrieve and select cases. The third section discusses implementation and the performance of the system, and the fourth section presents the conclusions and discusses future research.

SYSTEM ARCHITECTURE In this system, an overview of the system design with detailed descriptions of the major components of the system is presented. The system architecture is shown in Figure 1. The current system consists of three main components: an input module, a case memory, and a case retriever. The case retriever itself has three components: the indexing/matching module, the approximate selection module, and the adaptation module. The system is developed using the C language and the user interface is menu driven.

The processing starts with the input module which accepts new cases. The input module will present a blank risk behavior form on the screen as Figure 2 shows. The form helps AIP specialists categorize and organize aspects of the AIA domain in a manner that is consistent across AIA sessions. The form has numerous questions which are answered by filling in the blanks or checking boxes. It is not necessary for the user to completely answer every question, and missing data values are expected. The new case is entered into this form to start a session. Once the new case has been entered, the module passes the case to the indexing/matching module, which is responsible for retrieving relevant cases from the case memory by computing indexes and match scores. Next, the approximate selection module will choose the most relevant and useful case out of the set of candidate cases retrieved by the indexing/matching module. The adaptation module is able to modify the most relevant case selected by the approximate selection module to fit the new case. The adjustment rules provide the adjustment necessary to compensate for the differences of previous cases from the new case. After this step, the system uses the adapted best match to evaluate the new case. After the new case, i.e. a new subject, is screened, the system stores the case in the case memory, thereby expanding its expertise and integrating learning as well.

Case memory The AIA screening cases are descriptions of AIDS-risky individuals who have been screened in the past. A case consists of the risk behaviors, such as drug and sex behaviors, as well as assessment results. The cases are converted to the case memory in their original form, just as the AIP specialists presented them.

input Module

Input

I ndexin9 Metchln9

New

Module

Begin

Csee

I

4cleptetlon

Approxlmete .~

8e|eotlon

..~

Module

Store =

Dlopley

=

New Case

Module

I Edit

Figure 1 System architecture

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C a s e - b a s e d r e a s o n i n g for AIDS initial a s s e s s m e n t : L D Xu

Figure 2

B l a n k d a t a entry f o r m for risk a s s e s s m e n t

Although most screening cases are useful in CBR, the storage of all cases (in thousands) can lead to an overly large case memory~L The case memory is therefore intended to store a representative set rather than a complete set of cases. Since the case memory only captures those representative cases, the memory size is kept relatively small. The cases are ordered sequentially instead of in a more sophisticated way, such as the memory organization packet (MOP) 12. The case memory is currently populated with 50 cases. All the instances of the case memory are named by hyphenating the word 'case' and a number. Figure 3 shows a sample case stored in the case memory. In the case memory, each case is represented with a set of features which it is important to match among previous cases and the new case. A feature generally defines a particular characteristic of risk behavior. It is a triple of a feature type, a value, and a weight. The feature type specifies whether a feature is numerical or conceptual, feature values are used to make the comparison, and feature weights indicate the relative im=[.] Library Case Number: CASE-I Case ID: 123456789 Sex: Male Age: 22 Sexual Partner(s): Single Inject Drug (self): No Inject Drug (partner(s): Y e s Drug Treatment (self): N/A Drug Treatment (partner(s): No ELISA I: + ELISA 2: +

[t]~----I

= • o :!!::!!!!!i~!ii~i!i!::~!iiii!::!i~ii~!!i!i!!i!i!!~ ~i~i~iii~!~!::~!: i: :~iii!~!i!~!i!i~iiiiii::i iliiii::iii!i!i!iii!iii!i!ii!!i~::!::ii~i~i~i!!i i!!iiiii~i!!~i!~i:~i~i~iiiiiii~iiiiii~iii!iii!i::i!iiii~i~i~i~i~il • ._J !:: : : : ~ : ~ i: :: : ~ : : :: ~i:i i:~:~: : : :: ::::::::::::::::::::::::::: : : : : :~ : : : : i !:i ! : i !:i:i:!:~ : : : : :~ : i : i i:i:i:!:~:~:!:~:~:~:~:~ : i : : i ~ i i:i !i ::~:~:!:~:~:~:~:i:~:

Figure 3

34

S a m p l e case stored in case m e m o r y

portance of matching the feature. Features match when they have equal types and their values satisfy some comparison function. A feature is associated with the case as an index. In other words, cases are indexed by appropriate features so that cases can be retrieved with an appropriate probe. In general, cases indexed by subsets of the feature set are recalled.

Case retriever The main activities of the system are case retrieval and case selection. The retrieval process forms the basis for possible reasoning by retrieving episodic memories similar to the new case.

Indexing/matching module The indexing/matching module retrieves cases that are similar to the new case. The process of case retrieval is performed in three steps: (a) retrieving only those comparable cases that match the important indices of the new case 13, (b) calculating aggregate match scores for those comparable cases, and (c) selecting those comparable cases with higher aggregate match scores. Retrieving comparable cases involves assessing not only how close the previous cases are to the new case but the relative importance of the features as well. The risk behavior submodule and the approximate matching submodule are two core submodules in the indexing/ matching module. The risk behavior submodule maintains a set of features of risk behaviors. Features can be real numbers

K n o w l e d g e - B a s e d S y s t e m s V o l u m e 8 N u m b e r 1 F e b r u a r y 1995

Case-based reasoning for AIDS initial assessment: L D Xu

or concepts. Numeric values fall into prespecified ranges. Discrete concepts such as conventional roles fall into classes since a real-world concept does not exist in isolation; it exists with numerous relationships with other concepts. Inheritance is used to represent the hiearchical relationships between classes. In AIA, for example, conventional roles could be distinguished between worker and spouse; the spouse could be further subdivided into father and mother. These layers of divisions within division from a class hierarchy (see Figure 4). Sometimes the existing set of features may be limited since salient features of the new case that could constitute good features may not directly match the existing feature set. What may be important is some value calculated on the basis of the relationship between several field values. These are called derived features ]4. The knowledge provided by the AIP specialists is able to connect a field to other fields in the form of functions. Many of these functions are calculated by I F - T H E N ELSE rules. For example, if the age of drug injection initiation is important only when condition x is met, a calculated field, say INJECT-AGE-IF-X, is generated whose value is ignored when x is not met. In addition, the risk behavior submodule organizes features into three categories of importance and the degrees of importance are also represented as weights. Features that fit into the very important category are the ones that define the applicability of the case. Those previous cases that do not share very important features with the new case are generally considered incomparable. If there is a mismatch in this category, the case is rejected. The important category consists of features that should match in order for the case to be useful in reasoning the new case. If a mismatch in this category is identified, the knowledge in the adaptation module will be applied to modify the case to fit the new case. The less important category contains features for which an ability to match does not affect the selection of the case. The approximate matching submodule conducts matching of cases as well as returning the results of the case examinations. The matching is generally characterized as a partial matching process ~5. The approximate matching submodule prefers cases that (a) match on very important/important features over those that match on less important features, (b) match on a larger set of very important/important features over those

matching on a smaller set, and (c) match more specifically over less specific matches. Fuzzy set theory has been emphasized by recent work on knowledge-based systems including CBR systems 9,]6-18. In this system, the computation of partial matching is implemented by using a fuzzy retrieval algorithm (Algorithm I). The motivation for the application of fuzzy set theory to the matching process lies in the need to handle information that is less than ideal in the sense of being vague, imprecise, incomplete, and so on 9. Algorithm I can manipulate case parameters and subjective expert opinions in linguistic terms. This type of information is quite useful when the CBR system is to be used as an assessment aid in AIA where imprecise and subjective data are not only common but quite valuable. In the matching process, Algorithm I compares the features of the new case with features associated with previously stored cases and retrieves cases with respect to feature relevance (the relevance

(1)

Set V = {h, Y2, Y3} z {extremely relevant, very relevant, relevant} as a set of the degree of relevance.

(2)

Set F = ~ , f2, f3 . . . . f,} as a set of features.

(3)

Set the fuzzy evaluation matrix R (ri/)3 > ,r

(4)

Calculate the distribution of the feature weights W = { W(f,), W(f2). . . . . W(f,)}.

(5) The individual feature set varies from case to case. Let S denote the grade of membership, S = {S~), S(f2), ..., S(f,)}. 1 is assigned to the feature that is available, while 0 is assigned to the feature that is not available. (6)

Calculate W.S = W'. According to W 'o R -- B or n

Y~ W ' ( f i) o rij = B ( v j )

j = 1, 2, 3

j=l

Calculate n ~- {n(l~l) , n ( v 2 ) , B(Y3}

/ /N

Conventional

(7)

Spouee

Father

Figure 4

Worker

Mother

Classes

Role

Normalize B(vj) (/= 1,2,3). B(v/) is the aggregate match score for vj.

between features and the degree of risk behavior) as well as feature weights. Algorithm I is as follows. Once all the match factors have been determined, calculation is performed in a number of steps, such as Steps 4-7, to obtain the aggregate match scores. The aggregate match scores are sorted in descending order and stored in the A G G R E G A T E - M A T C H - S C O R E slot of the corresponding case. The retrieved cases are the ones with the higher aggregate match scores;

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Case-based reasoning for AIDS initial assessment: L D Xu

however, they are only candidate cases, rather than the best matches.

Approximate selection module As soon as a set of partial matches have been retrieved, two problems arise: (a) partial retrieval sometimes leads to the retrieval o f too many relevant cases, and (b) how does the system determine which case out of a group of cases best satisfies a set of criteria, if each of the cases satisfies some of the criteriaS,l°? Sycara mentioned that the problem can be addressed by the use of a multiobjective type algorithm for the selection of cases 1°. In this system, as a set of partially matching cases are retrieved by the indexing/matching module, it is presented to the approximate selection module for further screening. The task of the selection module, then, is to determine which of the partially matching cases retrieved is potentially the best match. The selection module will know about the relative importance of cases and be able to find a case that qualitatively maximizes the potential of creating relevant reasoning with respect to the new case. To determine the best match, Algorithm II is used to determine how close the retrieved case is to the new case in terms of a set of criteria. Algorithm II is as follows.

Xn} as a set of candidate

(1)

Set X cases.

(2)

Set Y = {Yl, Y2

(3)

Set the fuzzy evaluation matrix R : X × Y ---) [0,1], r~ = R (xi.yj) ~ [0,1]. Rlxi(ril, r,~,...,rim) [0,1] m. r,j denotes the grade of membership of the jth criterion of the ith case.

(4)

Set the evaluation function fi[0,1]m ~ R as E : f(zl, z2..... zm) and an approximate attainment measure of E for the preferred properties.

(5)

Calculate the overall evaluation function E(xi) -- fir,, r,a,..., rim) including an appropriate attainment measure (i ~
:

{Xl,

x 2.....

.....

Ym} as a set of criteria.

Adaptation module At this stage, the best match selected by the approximate selection module is compared to the new case so that deviations from the new case can be identified and appropriate adaptation can be generated. To obtain the adjustment needed to compensate for the differences between the best match and the new case, the system uses domain-specific knowledge in rule form to adapt the best match to the new case 2°. The AIP specialists provide the heuristic knowledge to adapt the best matching case that is the most similar in terms of risk behavior to the subject being assessed. The adjustment amounts for differences in each feature are thereby obtained. Once all adjustments are obtained, they are added or subtracted as appropriate. In this way, an adjusted value of risk behavior which better reflects the new case is produced. It is possible that the information needed to adjust a best matching case is not available, and in this case the comparable case is considered invalid and the adaptation module abandons the case. It then requests an alternative from the approximate selection module. The steps for parameterized adaptation comprise (a) the identification of those features in the best matching case that do not sufficiently match the new case, (b) the activation of the appropriate rules to identify the amount of adjustment, and (c) the application of the adjustment amounts to the best matching case to establish the adjusted risk value for the new case. The first step is to identify which features in the best matching case need adjustment. The rules use the match factor determined for each feature during case retrieval to decide whether to make an adjustment. In the second step, the adaptation rule is accessed as the particular feature with which it is associated needs an adjustment and is triggered when the condition it represents is met. In the third step, the adjustment for the best matching case is determined and the percentage with respect to risk behavior is obtained. Since the third step is generally able to provide an approximate risk value, a case reasoner is not included in the system 14. The third step presents the adjusted risk value directly to the AlP specialists who may make any needed modifications to adapt it to the current situation.

IMPLEMENTATION

Sample run The modeling aspects as well as the mathematical p r o o f of Algorithm II has been discussed in detail 19. The set of criteria includes maximizing the number of matches, minimizing the number of mismatches, maximizing the applicability as well as adaptability, etc. The required data are provided by Step 3 and Step 5 of Algorithm I. In Step 4 of Algorithm II, the evaluation data of those candidate cases are displayed on the screen, thus providing users with some interactivity 11 .18 . An edit screen allows the user to manually enter his/her judgment on those candidate cases, which is called the approximate attainment measure. The best match is found as soon as Step 5 proceeds.

36

To test the system, parameters, such as the feature relevancy, feature weight, match factor, and adjustment amounts for adaptation rules, are property set, with the completion of the descriptions for the case memory. The parameters are derived from interviews with AIP specialists who provide detailed information on the above-mentioned case attributes. To verify these derived parameters, a set of previous cases with assessment results are randomly selected. First, the derived parameters are checked against actual parameters in these cases for completeness and accuracy. If there were a parameter that was used in such cases but not listed in the derived parameters, this could indicate that the par-

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Case-based reasoning for AIDS initial assessment: L D Xu

ameter derivation omitted appropriate information. The parameter verification requires that the parameters that are supposed to be included are included. Second, the cases are treated as parameter-testing cases. Risk assessments are made for these cases using derived parameters. Then, the resulting assessments are compared with the previous case solutions. The parameter verification is successful given the set of test cases. On a set of 30 test cases, the parameters are found to be 98% complete and accurate. As the new case is entered into the system through the blank data entry form shown in Figure 2, the case retrieval phase begins. The features of the input case are used to retrieve relevant cases from the case memory that can be used as a base for further screening. Retrieval requires the activation of only those cases in memory that are associated with the salient features contained in the new case. The features and match factors of each comparable case are stored in order to indicate how close the value is of comparable cases in matching the value of corresponding features with the new case. Using Algorithm I, the aggregate match scores for selected cases in the case memory are calculated, and those cases with the higher aggregate match scores are retrieved. This is the output of the indexing/matching phase.The second phase is to determine the best matching case by employing Algorithm II. In the third phase, selected features receive the appropriate adjustment following the IF-THEN rules for both numeric and conceptual features and immediately provide the calculation of adjusted values. In the following example, Case-021 is the memory resident case, while Case-989 is the new case. The characteristics of these two cases include the following: (a) numeric values as well as concepts are involved, (b) some features are imprecise and need fuzzy mathematical treatment9, and (c) feature sets are different, and therefore adaptation is required.

Case-021: Case ID: Sex: Age: Sexual partner(s): Inject drug (self): Inject drug (partner(s)): Drug treatment (partner(s)): Elisa 1: Elisa 2: Western blot: Role: Role retention:

The test run concluded that CASE-989 is AIDS-risky and eligible to participate in the AIP project.

Test results Testing is one of the most important steps for a CBR system since it gives the AIP specialist a way of evaluating system generated results in the real world. First, if the case does not need any modification in memory, the system retrieves exactly the case the AIP specialists would have chosen. Second, if such a case is not in memory, the system retrieves the closest matching case. Third, it is found that the more similar the cases are that are retrieved to match a new case, the better is the matching quality as well as the quality of the assessments. Such a finding may exist for an obvious reason, i.e. it may be that the more similar the cases are, the more likely it is that a better match can be found. This provides evidence about the importance of the quantity of the cases in the case memory in relation to the performance of the system. On the other hand, there are substantial tradeoffs that can occur between the number of cases in the case memory and the matching speed. Fourth, when comparing the system-generated risk value for the subject and the risk value estimated by the AIP specialist, an insignificant difference is noted which averages about 1-9% of the risk value provided by the AIP specialists. The testing results are considered acceptable because the system-generated risk values are fairly consistent and close to the value provided by the AIP specialists.

CONCLUSIONS 123456789 male 22 single no yes no + + worker strong

Case-989: Case ID: Sex: Age: Sexual partner(s): Inject drug (self): Inject drug (partner(s)): Drug treatment (partner(s)): Elisa 1: Elisa 2: Role:

Role retention: weak Age of drug injection initiation: 16 Incarceration history: 7 months

123456780 female 28 multiple no some do, some don't some do, some don't + wife

The system presented in this paper is an ideal knowledge-based system for assessing risk behaviors in AIA. The advantages of the system include the following: (a) the system has reduced the average time needed to assess AIDS-risky behavior, (b) the CBR paradigm matches the thought processes with which AIP specialists administer AIA screening (AIDS-risky behavior can now be screened by retrieving case(s) stored in the case memory), (c) successfully adapted cases are stored so that they can be retrieved and reused in the future, and (d) cases can be used to train AIP professionals. When an entry-level AIA specialist joins an AIP program, an important part of his/her training involves going through those previous cases in the case memory. In the AIA domain, it is generally accepted that good practice in the assessment process is to explain all the subject's characteristics. To accomplish this goal, most screening tasks appear to involve (a) well defined knowledge (e.g. Western blot tests) that can be expressed as rules, (b) less well defined experiences (e.g. the effect of the age of drug injection initiation on risk behavior) from which it is difficult to establish general rules or regularities, and (c) additional knowledge that Knowledge-Based Systems Volume 8 Number 1 February 1995

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Case-based reasoning for AIDS initial assessment: L D Xu

needs to be organized on the basis of the first two categories. In other words, based on the static rule-based knowledge and the specific case context, a more comprehensive analysis needs to be generated. It is true that, in some cases, rule-based knowledge may contain sufficient manifestations to make a correct assessment 7. However, for those cases in which comprehending the complex structure of data of both rule-based and casebased knowledge is essential to an assessment, using either rule-based or case-based knowledge will omit important information. The examples provided earlier appear to consist of both well defined knowledge (e.g., in Case-021, two positive Elisa tests plus one negative Western blot test tend to indicate risk behaviors) and less well defined knowledge (e.g. risk factors such as the age of drug injection and incarceration history in Case 989 (no exact rules or regularities can be concluded from such factors)). The AIP specialists need to organize a body of knowledge based on rule-based and case-based knowledge. Additional knowledge (e.g. a statistical analysis of the effect of the age of drug injection and incarceration history on risk behavior) needs to be activated in the specific case context to reconfigure and extend the rule-based and case-based knowledge for complex assessment situations. Assessments based only upon rule-based knowledge are subject to incompleteness (e.g. a rule may have exceptions or a rule base may not include less well defined knowledge), while assessments made only from casebased knowledge may be less systematic (e.g. some important rules or algorithms may not be used in existing cases). AIA knowledge can be viewed as a collection of rule-based and case-based knowledge, each with an embedded assessment mechanism. A satisfactory accounting of an overall assessment can be done only after both rule-based and case-based knowledge are identified and analyzed. If rule-based and case-based systems were integrated into a single system, it could use all the available information from rule-based and case-based systems to achieve expert assessment perfor-

REFERENCES I

2

3

4

5

6

7

8 9

10

11

12 13

14 15

mance21,22.

Currently, the system is being improved from a number of viewpoints. First, the system is being integrated with the rule-based AIA system to continue both inductive and deductive approachesT; thus it makes the reasoning process more knowledgeable and avoids reasoning based only upon previous experiences2L The architecture of the integrated system consists of the three original components (see Figure 1) plus a rule base. Second, relational databases are being used as case memory to meet the system requirements for a larger database. Third, the interface is being designed to emphasize multimedia techniques. Fourth, in the case memory, selected cases are being broken down into smaller sections. Such sections can be merged to reconstruct the whole as well as form new solutions ~8,22.

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