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Lecture 06 Case-Based Reasoning

• Topics– Case-based Reasoning Paradigm– Case as a Knowledge Representation

Technique– Case Retrieval– Case Selection– Case Adaptation– Case Learning– Case Retaining and Maintenance– Applications

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Case-based reasoning paradigm

• CBR architectureProblem

specification

Caseretrieval

Caseselection

Caseadaptation

Caselearning &retaining

Caselibrary

Adaptationplan

library

Adaptedsolution

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Case-based reasoning paradigm

• Advantages– Cases are easier to obtain than domain

knowledge– Most efficient if old solutions directly

apply– Efficient if little adaptation effort is

involved– Learning of case-specific knowledge is

possible– Learning of new cases is possible– A very good choice for supplement

reasoning mechanism

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Case as a KR technique

• A case typically consists of a problem specification, solution, and case-specific meta-knowledge

• Example case for medical diagnosis– Patient model, containing subjective findings,

objective findings, and pathology and laboratory examinations

– Diagnosis model, recording the scenario of how a diagnosis proceeds and the diagnosis result

– Specific adaptation knowledge, specifying the causal relationships among the symptoms, test data, and diseases; helping hypothesize the suspected diseases from the adapted case data.

– Differential diagnosis knowledge, helping differentiate diseases with analogous symptoms

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Case as a KR technique

•Patient modelI. Subjective findings:

1. Personal history: recurrent pneumonia2. Chief complaint: cough

II. Objective findings:1. Present illness: fever, dyspnea, hemoptysis,

chest pain2. Physical examination:

A. Temperature: 39.5C, Fever

B. Thorax and lungs: crackles abnormal breathing sound, …

C. Extremity: clubbing fingers

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Case as a KR technique

III. Pathology and Laboratory data:1. CBC-DC:

A. Hb: 13.5(g/dl) B. RBC: 4.17*106 C. WBC: 16,350 … 2. Biochemistry

A. A/G: 3.0/2.8, B. LDH: 0.5, C. CK: 30(U/L) D. CK-MB: 3(U/L) …3. Microbiology

A. Sputum: Bloody B. Culture: Pneumococcus …4. Body fluid analysis of pleural effusion

A. PH: 7.4 B. SG: 1.037 C. Protein: 3.8 D. Appear: Yellow clear …

IV. Imaging:1. Chest x-ray: Honeycombing appearance consolidation patch

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Case as a KR technique

• Diagnosis modelDisease type: RespiratoryAffected organ: PulmonaryImpression: Bronchitis, Pleural effusion, Hypersensitivity pneumonitis

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Case as a KR technique

• Case-specific adaptation knowledgeR1: IF (Input feature D = {x}) & (x V3) THEN D = {V3} R2: I F (disease type {respiratory}) & (present illness {fever, cough, sputum}) & (sputum smear = {gram negative}) THEN disease = {pneumonic(bacterial)}R3: I F (disease type {respiratory}) & (present illness {dyspnea}) THEN Imaging = {chest x-ray}

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Case as a KR technique

• Differential diagnosis knowledgeIF (B=V21) and (A=V12) and (D=V32) and (E=V42) THEN Solutions {S2}

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Case retrieval

• Case similarity measure• Vector space model

– p = vector of problem feature values – c = vector of case feature values– Values: numerical vs. categorical– Similarity function

– Θ: any function that return a number representing feature similarity between pi and ci

– i: weight of feature i

– normalization factor

i

iii cpcpsim ),(

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Case retrieval

• Enhanced by fuzzy logic– Feature values are fuzzy sets– Fuzzy similarity measure is calculated

according to fuzzy relationships – Example fuzzy relationships

Critical 1.00Severe 0.02 1.00Average 0.03 0.50 1.00Slight 0.01 0.50 0.75

1.00… … … … …

Slight Average Severe Critical

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Case retrieval

• Cluster-based similarity– Cluster selection: K-means, KNN, NN– Feature values are coded and fed into

the cluster for clustering (sometimes we may do classification)

– All cases Use are properly categorized into clusters

– Use the cluster of the problem to retrieve cases

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Case selection

• Select cases as candidate cases for adaptation by utility

• Utility = Similarity * Adaptability– Similarity measure as calculated before– Adaptability as a moderator to shape

the similarity

• Adaptability measure– Measure the degree of adaptability of a

feature according to feature value difference

– Take summary of average of the degrees into a case adaptability

)iWeight(FDiffer)iDiffer(FValue )iAD(F __1

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Case adaptation

• Adaptation methods• A single candidate case

– Similarity-based adaptation– Analogy-based adaptation– Derivation-based adaptation

• Multiple candidate cases– Combine cases into solutions

• Cases are not broken down

– Planning• Use a data structure to break down and

structure all candidate cases

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Case adaptation - Similarity-based feature adaptation operators

• Substitution refers to adaptation operators which replace or adjust a feature value

• Example operators– Parameter adjustment

• Adjust the old value to a new one with different significance.

• Precondition: <1, 0, ≠0, x, x>• Postcondition: <0, 0, 0, x, 1>

– Problem abstraction• Substitute in a high-level value in the domain

ontology.• Precondition: …; Postcondition: …

– Problem refinement• Substitute in a low-level value in the domain

ontology.• Precondition: …; Postcondition: …

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Case adaptation

• Transformation refers to restructuring, specialization, generalization, process refinement, domain enlargement, value modification or constraint change.

• Example operators– Constraint abstraction

• Generalize the feature value for a constraint into a higher-level value in the domain ontology.

• Precondition: …; Postcondition: …– Constraint deletion

• Delete the constraint if it conforms to the conditions.

• Precondition: …; Postcondition: …– Value specialization

• Specialize the old value into a new one if they belong to the same solution type and significance.

• Precondition: …; Postcondition: …

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Case adaptation

• Generation refers to regeneration or appending of features and/or values.

• Example operator– Value insertion

• Replace the unknown value with a new value.• Precondition: …; Postcondition: …

• Pre- and post-Condition measures the differences between cases and problem– Value difference measures how close feature

values are – Proximity difference measures how close a

feature is related to a disease– Seriousness difference of numeric value– Specificity difference about number of cases– Constraint violation measures the amount of

constraints or causal relations violated

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Case adaptation

• Planning method for adaptation– Candidate cases are broken down into a set of

related features, each with a (adaptation) utility– Develop a feature adaptation plan for each

feature in all candidate cases (consult adaptation plan library)

• Select a feature adaptation operator according to the feature difference measures

– Aggregate feature adaptation plans into a case adaptation plan

• according to the preconditions and postconditions of the feature adaptation operators

– Execute the case adaptation plan to generate a new case (adapted case)

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Case learning• Knowledge discovery from candidate

cases– Domain knowledge

• Ex: differential diagnosis knowledge by finding feature value differences among (similar) candidate cases

• Ex: diagnosis rules by mining associations among candidate cases

– Adaptation knowledge• By generalizing feature values appearing in a set

of candidate cases– Meta-knowledge

• Ex: case-specific verification knowledge by discovering associations of feature values which always appear together

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Case retaining and maintenance

• Retaining of adapted cases in case library– User evaluation

• Evaluate how an adapted case work for the given problem

– Case similarity• Case library should only contain

representative cases

• Maintenance of case library– Case credit

• User feedback on how a case performs – Case age

• Time-based or application history-based calculation of case age

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Applications

• Synthetic task– Design new shoes– Cook a new dish

• Analytic task– Medical diagnosis– ECG diagnosis