MYCIN was an early expert system developed in the 1970s and 1980s to diagnose bacterial infections and recommend antibiotics. It used a production rule-based approach with a static knowledge base of rules and a dynamic knowledge base to represent patient-specific information. MYCIN could explain its reasoning and allow knowledge engineers to update its rules through an interactive dialogue interface. The system demonstrated competence comparable to human experts in bacterial infection diagnosis and treatment selection.

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. Plan
• History
• MYCIN : The Problem
• System Goals
• Why Mycin ?
• MYCIN Architecture
• Consultation System
• Static Database
• Dynamic Database
• Explanation System
• Knowledge Acquisition
• Results
• Conclusion

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Presented By : Krim Rached Mail@:Rached.krim@gmail.com At Bechar Le 21/04/2014
At Bechar 21/04/2014