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Introduction To Mycin Expert System

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Mycin Expert Systems Basics , Features , Components And Advantages , Disadvantages, Basically How Actually Mycin Expert System Works

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Introduction To Mycin Expert System

  1. 1. PRESENTED BY NIPUN JASWALMYCIN was an early expertsystem that used artificialintelligence to identifybacteria causing severeinfections, such asbacteremia and meningitis,and torecommend antibiotics, withthe dosage adjusted forpatients body weight — thename derived from theantibiotics themselves, asmany antibiotics have thesuffix "-mycin". The Mycinsystem was also used forthe diagnosis of bloodclotting diseases.MYCIN was never actuallyused in practice. This wasntbecause of any weakness in itsperformance. As mentioned, intests it outperformed membersof the Stanford medical schoolfaculty. Some observers raisedethical and legal issues relatedto the use of computers inmedicine — if a program givesthe wrong diagnosis orrecommends the wrongtherapy, who should be heldresponsible? However, thegreatest problem, and thereason that MYCIN was notused in routine practice, wasthe state of technologies forsystem integration, especiallyat the time it was developed.MYCIN EXPERT SYSTEMwww.nipunjaswal.com How Helpful Are Expert Systems In Medical ? 20, Apr 2013
  2. 2. Your LogoWhat is to be Covered? History and Overview MYCIN Architecture Consultation System Explanation System Knowledge Acquisition Results, ConclusionsTOPIC : MYCIN E.S
  3. 3. Your LogoHistory Thesis Project by Shortliffe from Stanford Davis, Buchanan, van Melle, and othersStanford Heuristic Programming ProjectInfectious Disease Group, Stanford Medical Project Spans a DecadeResearch started in 1972Original implementation completed 1976Research continues into the 80’s as wellTOPIC : MYCIN E.S
  4. 4. Your LogoWhy Mycin ? Disease DIAGNOSIS and Therapy SELECTION Advice for non-expert physicians with time considerations and incompleteevidence on:Bacterial infections of the bloodExpanded to other ailmentsTOPIC : MYCIN E.S
  5. 5. Your LogoSystem Goals UtilityBe useful, to attract assistance of expertsDemonstrate competence FlexibilityDomain is complex, variety of knowledge typesMedical knowledge rapidly evolves, must be easy to maintain K.B.TOPIC : MYCIN E.S
  6. 6. Your LogoSystem Goals Interactive DialogueProvide easy explanationsAllow for real-time K.B. updates by experts Fast and EasyMeet time constraints of the medical field6TOPIC : MYCIN E.S
  7. 7. Your LogoMYCIN Architecture7TOPIC : MYCIN E.S
  8. 8. Your LogoConsultation System  Performs Diagnosis andTherapy Selection Control Structure readsStatic DB (rules) andread/writes to Dynamic DB(patient, context) Linked to Explanations Terminal interface toPhysician8TOPIC : MYCIN E.S
  9. 9. Your LogoConsultation System User-Friendly Features:Users can request rephrasing of questionsSynonym dictionary allows latitude of user responses Questions are asked when more data is neededIf data cannot be provided, system ignores relevant rules9TOPIC : MYCIN E.S
  10. 10. Your LogoConsultation “Control Structure” High-level Algorithm:1. Determine if Patient has significant infection2. Determine likely identity of significant organisms3. Decide which drugs are potentially useful4. Select best drug or coverage of drugsTOPIC : MYCIN E.S
  11. 11. Your LogoStatic Database  Rules Meta-Rules Templates Rule Properties Context Properties Fed from KnowledgeAcquisition System11TOPIC : MYCIN E.S
  12. 12. Your LogoProduction Rules Represent Domain-specific Knowledge Over 450 rules in MYCIN Premise-Action (If-Then) Form Each rule is completely modular, all relevant context is contained in the rulewith explicitly stated premises12TOPIC : MYCIN E.S
  13. 13. Your LogoMYCIN Assumptions Not every domain can be represented, requires formalization IF-THEN formalism is suitable for Expert Knowledge Acquisition andExplanation sub-systems13TOPIC : MYCIN E.S
  14. 14. Your LogoJudgmental Knowledge Inexact Reasoning with Certainty Factors (CF) CF are not Probability! Truth of a Hypothesis is measured by a sum of the CFsPremises and Rules added togetherPositive sum is confirming evidenceNegative sum is disconfirming evidence14TOPIC : MYCIN E.S
  15. 15. Your LogoPreview Mechanism Interpreter reads rules before invoking them Avoids unnecessary deductive work if the sub-goal has already beentested/determined Ensures self-referencing sub-goals do not enter recursive infinite loops15TOPIC : MYCIN E.S
  16. 16. Your LogoMeta-Rules Alternative to exhaustive invocation of all rules Strategy rules to suggest an approach for a given sub-goalOrdering rules to try first, effectively pruning the search tree Creates a search-space with embedded information on which branch is bestto take16TOPIC : MYCIN E.S
  17. 17. Your LogoMeta-Rules (continued) High-order Meta-Rules (i.e. Meta-Rules for Meta-Rules)Powerful, but used limitedly in practice Impact to the Explanation System:(+) Encode Knowledge formerly in the Control Structure(-) Sometimes create “murky” explanations17TOPIC : MYCIN E.S
  18. 18. Your LogoTemplates The Production Rules are all based on Template structures This aids Knowledge-base expansion, because the system can“understand” its own representations Templates are updated by the system when a new rule is entered18TOPIC : MYCIN E.S
  19. 19. Your LogoDynamic Database  Patient Data Laboratory Data Context Tree Built by ConsultationSystem Used by ExplanationSystem19TOPIC : MYCIN E.S
  20. 20. Your LogoTherapy Selection Plan-Generate-and-Test Process Therapy List Creation Set of specific rules recommend treatments based on the probability(not CF) of organism sensitivity Probabilities based on laboratory data One therapy rule for every organism20TOPIC : MYCIN E.S
  21. 21. Your LogoTherapy Selection Assigning Item NumbersOnly hypothesis with organisms deemed “significantly likely” (CF) areconsideredThen the most likely (CF) identity of the organisms themselves aredetermined and assigned an Item NumberEach item is assigned a probability of likelihood and probability ofsensitivity to drug21TOPIC : MYCIN E.S
  22. 22. Your LogoTherapy Selection Final Selection based on:SensitivityContraindication ScreeningUsing the minimal number of drugs and maximizing the coverage oforganisms Experts can ask for alternate treatmentsTherapy selection is repeated with previously recommended drugsremoved from the list22TOPIC : MYCIN E.S
  23. 23. Your LogoExplanation System Provides reasoningwhy a conclusion hasbeen made, or why aquestion is beingasked Q-A Module Reasoning StatusChecker23TOPIC : MYCIN E.S
  24. 24. Your LogoExplanation System Uses a trace of the Production Rules for a basis, and the Context Tree, toprovide contextIgnores Definitional Rules (CF == 1) Two ModulesQ-A ModuleReasoning Status Checker24TOPIC : MYCIN E.S
  25. 25. Your LogoReasoning Status Checker Explanation is a tree traversal of the traced rules:WHY – moves up the treeHOW – moves down (possibly to untried areas) Question is rephrased, and the rule being applied is explained with thetranslation patterns25TOPIC : MYCIN E.S
  26. 26. Your LogoKnowledge Acquisition System Extends Static DB viaDialogue with Experts Dialogue Driven bySystem Requires minimaltraining for Experts Allows for IncrementalCompetence, NOT anAll-or-Nothing model26TOPIC : MYCIN E.S
  27. 27. Your LogoKnowledge Acquisition IF-THEN Symbolic logic was found to be easy for experts to learn, andrequired little training by the MYCIN team When faced with a rule, the expert must either except it or be forced toupdate it using the education process27TOPIC : MYCIN E.S
  28. 28. Your LogoResults Never implemented for routine clinical use Shown to be competent by panels of experts, even in cases where expertsthemselves disagreed on conclusions Key Contributions:Reuse of Production Rules (explanation, knowledge acquisitionmodels)Meta-Level Knowledge Use28TOPIC : MYCIN E.S
  29. 29. Your LogoThanks !29TOPIC : MYCIN E.S

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