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Introduction To Mycin Expert System
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PRESENTED BY NIPUN JASWAL MYCIN was an early expert system that used artificial intelligence to identify bacteria causing severe infections, such as bacteremia and meningitis, and to recommend antibiotics , with the dosage adjusted for patient's body weight — the name derived from the antibiotics themselves, as many antibiotics have the suffix "-mycin". The Mycin system was also used for the diagnosis of blood MYCIN was never actually used in practice. This wasn't because of any weakness in its performance. As mentioned, in tests it outperformed members of the Stanford medical school faculty. Some observers raised ethical and legal issues related to the use of computers in medicine — if a program gives the wrong diagnosis or recommends the wrong therapy, who should be held responsible? However, the greatest problem, and the reason that MYCIN was not used in routine practice, was MYCIN EXPERT SYSTEM www.nipunjaswal.com How Helpful Are Expert Systems In Medical ? 20, Apr 2013
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Mycin Expert Systems Basics , Features , Components And Advantages , Disadvantages, Basically How Actually Mycin Expert System Works
Transcript of Introduction To Mycin Expert System
PRESENTED BY NIPUN JASWALMYCIN was an early expert system that used artificial intelligence to identify bacteria causing severe infections, such as bacteremia and meningitis, and to recommend antibiotics, with the dosage adjusted for patient's body weight — the name derived from the antibiotics themselves, as many antibiotics have the suffix "-mycin". The Mycin system was also used for the diagnosis of blood clotting diseases.
MYCIN was never actually used in practice. This wasn't because of any weakness in its performance. As mentioned, in tests it outperformed members of the Stanford medical school faculty. Some observers raised ethical and legal issues related to the use of computers in medicine — if a program gives the wrong diagnosis or recommends the wrong therapy, who should be held responsible? However, the greatest problem, and the reason that MYCIN was not used in routine practice, was the state of technologies for system integration, especially at the time it was developed.
MYCIN EXPERT SYSTEMwww.nipunjaswal.com How Helpful Are Expert Systems In Medical ? 20, Apr 2013
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What is to be Covered? History and Overview
MYCIN Architecture
Consultation System
Explanation System
Knowledge Acquisition
Results, Conclusions
TOPIC : MYCIN E.S
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History 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 well
TOPIC : MYCIN E.S
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Why Mycin ? 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
TOPIC : MYCIN E.S
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System 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
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System Goals Interactive Dialogue
Provide easy explanations
Allow for real-time K.B. updates by experts
Fast and Easy
Meet time constraints of the medical field
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MYCIN Architecture
ConsultationSystem
ExplanationSystem
KnowledgeAcquisition
System
Q-A System
Dynamic DBPatient DataContext TreeDynamic Data
Static DBRules
Parameter PropertiesContext Type Properties
Tables, Lists
Physician
Expert
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Consultation System
ConsultationSystem
ExplanationSystem
KnowledgeAcquisition
System
Q-A System
Dynamic DB Static DB
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
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Consultation 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 rules
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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
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Static Database
ConsultationSystem
ExplanationSystem
KnowledgeAcquisition
System
Q-A System
Dynamic DB Static DB
Physician
Expert
Rules
Meta-Rules
Templates
Rule Properties
Context Properties
Fed from Knowledge Acquisition System
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Production 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 rule with explicitly stated premises
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MYCIN Assumptions Not every domain can be represented, requires formalization
IF-THEN formalism is suitable for Expert Knowledge Acquisition and Explanation sub-systems
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Judgmental 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 evidence
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Preview Mechanism Interpreter reads rules before invoking them
Avoids unnecessary deductive work if the sub-goal has already been tested/determined
Ensures self-referencing sub-goals do not enter recursive infinite loops
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Meta-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 best to take
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Meta-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” explanations
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Templates 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
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Dynamic Database
ConsultationSystem
ExplanationSystem
KnowledgeAcquisition
System
Q-A System
Dynamic DB Static DB
Physician
Expert
Patient Data
Laboratory Data
Context Tree
Built by Consultation System
Used by Explanation System
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Therapy 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 organism
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Therapy Selection Assigning Item Numbers
Only hypothesis with organisms deemed “significantly likely” (CF) are considered
Then the most likely (CF) identity of the organisms themselves are determined and assigned an Item Number
Each item is assigned a probability of likelihood and probability of sensitivity to drug
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Therapy Selection Final Selection based on:
Sensitivity
Contraindication Screening
Using the minimal number of drugs and maximizing the coverage of organisms
Experts can ask for alternate treatments
Therapy selection is repeated with previously recommended drugs removed from the list
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Explanation System
ConsultationSystem
ExplanationSystem
KnowledgeAcquisition
System
Q-A System
Dynamic DB Static DB
Physician
Expert
Provides reasoning why a conclusion has been made, or why a question is being asked
Q-A Module
Reasoning Status Checker
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Explanation System Uses a trace of the Production Rules for a basis, and the Context Tree, to
provide context
Ignores Definitional Rules (CF == 1)
Two Modules
Q-A Module
Reasoning Status Checker
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Reasoning 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 the translation patterns
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Knowledge Acquisition System
ConsultationSystem
ExplanationSystem
KnowledgeAcquisition
System
Q-A System
Dynamic DB Static DB
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
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Knowledge Acquisition IF-THEN Symbolic logic was found to be easy for experts to learn, and
required little training by the MYCIN team
When faced with a rule, the expert must either except it or be forced to update it using the education process
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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 models)
Meta-Level Knowledge Use
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Thanks !
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