Expert System MYCIN

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Expert System MYCIN

  1. 1. Presented By : Krim Rached Émail@: Rached.krim@gmail.com Framed By: Belaguide .M At Bechar 22/04/2014 University Of Bechar Department of Exact Sciences Promotion : 1st year Master SIA
  2. 2. Plan • History • MYCIN : The Problem • System Goals • Why Mycin ? • MYCIN Architecture • Consultation System • Static Database • Dynamic Database • Explanation System • Knowledge Acquisition • Results • Conclusion
  3. 3. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 MYCIN was developed at Stanford U Project spans a decade  Research started in 1972.  Original Implementation completed in 1976  Research continued into the 1980 HISTORY
  4. 4. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Utility Be useful, to attract assistance of experts Demonstrate competence Flexibility Domain is complex, variety of knowledge types Medical knowledge rapidly evolves, System Goals 1/2
  5. 5. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 System Goals 2/2 Interactive Dialogue Provide easy explanations Allow for real-time K.B. updates by experts Fast and Easy Meet time constraints of the medical field
  6. 6. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014  Disease DIAGNOSIS and Therapy SELECTION Advice for non-expert physicians with time considerations and incomplete evidence on: • Bacterial infections of the blood • Expanded to other ailments Why Mycin ?
  7. 7. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Consultation System Explanation System Knowledge Acquisition System Q-A System Dynamic DB Patient Data Context Tree Dynamic Data Static DB Rules Parameter Properties Context Type Properties Tables, Lists Physician Expert MYCIN Architecture
  8. 8. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Consultation System Consultation System Explanation System Knowledge Acquisition System Q-ASystem DynamicDB PatientData ContextTree DynamicData StaticDB Rules ParameterProperties ContextTypeProperties Tables,Lists Physician Expert • Performs Diagnosis and Therapy Selection • Control Structure reads Static DB (rules) and read/writes to Dynamic DB (patient, context) • Linked to Explanations • Terminal interface to Physician
  9. 9. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Consultation “Control Structure” High-level Algorithm: 1. Determine if Patient has significant infection 2. Determine likely identity of significant organisms 3. Decide which drugs are potentially useful 4. Select best drug or coverage of drugs
  10. 10. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 • Rules • Meta-Rules • Templates • Rule Properties • Context Properties • Fed from Knowledge Acquisition System Consultation System Explanation System Knowledge Acquisition System Q-ASystem DynamicDB PatientData ContextTree DynamicData StaticDB Rules ParameterProperties ContextTypeProperties Tables,Lists Physician Expert Static Database
  11. 11. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014  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 rule with explicitly stated premises Production Rules
  12. 12. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 • 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 best to take Meta-Rules
  13. 13. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 • 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 entered Templates
  14. 14. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Dynamic Database Consultation System Explanation System Knowledge Acquisition System Q-ASystem DynamicDB PatientData ContextTree DynamicData StaticDB Rules ParameterProperties ContextTypeProperties Tables,Lists Physician Expert • Patient Data • Laboratory Data • Context Tree • Built by Consultation System • Used by
  15. 15. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Patient-1 (person) Culture-1 (curculs) Culture-2 (curculs) Organism-1 (curorgs) Organism-2 (curorgs) Organism-3 (curorgs) Therapy-1 (possther) Operation-1 (opers) Drug-1 (curdrgs) Drug-2 (curdrgs) Drug-4 (opdrgs) Context Tree
  16. 16. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Explanation System • Provides reasoning why a conclusion has been made, or why a question is being asked • Q-A Module • Reasoning Status Checker Consultation System Explanation System Knowledge Acquisition System Q-ASystem DynamicDB PatientData ContextTree DynamicData StaticDB Rules ParameterProperties ContextTypeProperties Tables,Lists Physician Expert
  17. 17. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Reasoning Status Checker (Example) 32) Was penicillinase added to this blood culture (CULTURE-1)? **WHY [i.e. WHY is it important to determine whether penicillinase was added to CULTURE-1?] [3.0] This will aid in determining whether ORGANISM-1 is a contaminant. It has already been established that [3.1] the site of CULTURE-1 is blood, and [3.2] the gram stain of ORGANISM-1 is grampos Therefore, if [3.3] penicillinase was added to this blood culture then there is weakly suggestive evidence...
  18. 18. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Consultation System Explanation System Knowledge Acquisition System Q-ASystem DynamicDB PatientData ContextTree DynamicData StaticDB Rules ParameterProperties ContextTypeProperties Tables,Lists Physician Expert  Extends Static DB via Dialogue with Experts  Dialogue Driven by System  Requires minimal training for Experts  Allows for Incremental Competence, NOT an All- or-Nothing model KnowledgeAcquisition System
  19. 19. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Results  Never implemented for routine clinical use  Shown to be competent by panels of experts, even in cases where experts themselves disagreed on conclusions  Key Contributions:  Reuse of Production Rules (explanation, knowledge acquisition
  20. 20. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 Conclusion MYCIN is the first of a new generation of computer programs that due to the world, to explain their reasoning, and provide advice which is comparable to advice provided by human experts. The development of MYCIN brand a transition in AI research.
  21. 21. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar 21/04/2014 References • Davis, Buchanan, Shortliffe. Production Rules as a Representation for a Knowledge-Based Consultation System. Artificial Intelligence, 1979. • William van Melle. The Structure of the MYCIN System. International Journal of Man-Machine Studies, 1978. • Shortliffe. Details of the Consultation System. Computer-Based Medical Consultations: MYCIN, 1976.
  22. 22. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar Le 21/04/2014 At Bechar 21/04/2014

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