1. 06 -1 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

2. 06 -2 Case-based reasoning paradigm CBR architecture

3. 06 -3 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

4. 06 -4 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

5. 06 -5 Case as a KR technique Patient model I. Subjective findings : 1. Personal history: recurrent pneumonia 2. Chief complaint: cough II. Objective findings: 1. Present illness: fever, dyspnea, hemoptysis, chest pain 2. Physical examination: A. Temperature: 39.5  C, Fever B. Thorax and lungs: crackles abnormal breathing sound, … C. Extremity: clubbing fingers

6. 06 -6 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: 7.4B. SG: 1.037 C. Protein: 3.8 D. Appear: Yellow clear … IV. Imaging: 1. Chest x-ray: Honeycombing appearance consolidation patch

7. 06 -7 Case as a KR technique Diagnosis model Disease type: Respiratory Affected organ: Pulmonary Impression: Bronchitis, Pleural effusion, Hypersensitivity pneumonitis

8. 06 -8 Case as a KR technique Case-specific adaptation knowledge R1: 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}

9. 06 -9 Case as a KR technique Differential diagnosis knowledge IF (B=V21) and (A=V12) and (D=V32) and (E=V42) THEN Solutions  {S2}

10. 06 -10 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 p i and c i –  i : weight of feature i –  normalization factor

11. 06 -11 Case retrieval Enhanced by fuzzy logic –Feature values are fuzzy sets –Fuzzy similarity measure is calculated according to fuzzy relationships –Example fuzzy relationships Critical 1.00 Severe 0.02 1.00 Average 0.03 0.50 1.00 Slight0.01 0.50 0.75 1.00 … … … … … Slight Average Severe Critical

12. 06 -12 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

13. 06 -13 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

14. 06 -14 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

15. 06 -15 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: Postcondition: –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: …

16. 06 -16 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: …

17. 06 -17 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

18. 06 -18 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)

19. 06 -19 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

20. 06 -20 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

21. 06 -21 Applications Synthetic task –Design new shoes –Cook a new dish Analytic task –Medical diagnosis –ECG diagnosis