This document discusses the development and testing of the MYCIN expert system. MYCIN was developed in 1976 at Stanford University to diagnose and recommend treatment for bacterial infections. It was tested against physicians and found to be as or more accurate in its diagnoses and treatment recommendations. However, MYCIN was never fully implemented in clinical practice due to legal liability concerns if it provided incorrect diagnoses.
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This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
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This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
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# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
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Artificial Intelligence: Case-based & Model-based ReasoningThe Integral Worm
This presentation covers case-based and model-based reasoning for artificial intelligence. Topics covered are as follows: case-based reasoning, case-based reasoning components; case base, retriever, adapter, refiner, executor, and evalutator; and model-based reasoning.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
Presentation on "Knowledge acquisition & validation"Aditya Sarkar
Presentation on "Knowledge acquisition and validation made and presented by Aditya Sarkar, I took the help of different sources available on internet to make all understand how a knowledge is acquired?. I hope this presentation will help everyone.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Artificial Intelligence: Case-based & Model-based ReasoningThe Integral Worm
This presentation covers case-based and model-based reasoning for artificial intelligence. Topics covered are as follows: case-based reasoning, case-based reasoning components; case base, retriever, adapter, refiner, executor, and evalutator; and model-based reasoning.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
Presentation on "Knowledge acquisition & validation"Aditya Sarkar
Presentation on "Knowledge acquisition and validation made and presented by Aditya Sarkar, I took the help of different sources available on internet to make all understand how a knowledge is acquired?. I hope this presentation will help everyone.
Iceei2013 expert system in detecting coffee plant diseasesWahyu Nugraha
Coffee is an important commodity in the world economy. But unfortunately, productivity and quality of those commodities results are still quite low. This is caused by the disease in coffee plants.
The research objective is to create an application that can help researchers or observers working in coffee plantation to diagnose diseases of coffee plants.
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From the experiments, accuracy calculation of the system is about 85%. Based on the accuracy, it can be concluded that this application can be a bit much to help researchers or observers of the coffee plants in diagnosing coffee plants diseases earlier.
Index Terms— Expert System, Fuzzy, Decision Tree, Coffee, Disease
Los sistemas expertos son llamados así porque emulan el razonamiento de un experto en un dominio concreto, y en ocasiones son usados por éstos. Con los sistemas expertos se busca una mejor calidad y rapidez en las respuestas, dando así lugar a una mejora de la productividad del propio experto al usar este tipo de sistemas informáticos.
Concept of Expert Systems .
Appearing of Expert System.
Areas of Success and Failure.
Examples.
Advantages and disadvantages.
What have been planned for it in the future
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David Watwood - Ciga Healthcare
Marc Winrow - Lumos Diagnostics
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Basic computer and internet knowledge for electronic searching of the literature
EuroBioForum 2013 - Day 2 | Mark PoznanskyEuroBioForum
EuroBioForum 2013 2nd Annual Conference
27-28 May 2013 - Hilton Munich City, Munich, Germany
http://www.eurobioforum.eu/2013
=======================================
# REGIONAL PERSPECTIVES #
Ontario Genomics Institute, Canada:
Innovative Research, Innovative Translation
Dr Mark Poznansky
President and CEO Ontario Genomics Institute
=======================================
http://www.eurobioforum.eu
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
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Expert system mycin
1.
2. •In 1965 DENDRAL was
used in expert system
•MYCIN was developed in
1976
3. WHY MYCIN
Disease diagnosis and therapy selection
Advise for non expert physicians with time
consideration and incomplete evidence as
Bacterial infections of the blood
Extended to other elements
4. PROBLEM BEFORE MYCIN
• ROBERTS AND VISCONTI:-
Only 13% patients are treated rationally
66% are being given irrational treatment
21% are being given questionable treatment
• IRRATIONAL MEANS, FOR EXAMPLE: Using a contra-indicated combination
Using the wrong agent for a specific organisms
Not taking the required culture
5. DEVELOPMENT OF MYCIN
• Developed by ADWARD SHORTLIFFE in 1976
a STANFORD UNIVERSITY in the artificial intelligence
in medicine group
• First system that was implemented into the medical
field
6. HOW MYCIN WAS TESTED FOR
ACCURACY
• 10 patients for a particular disease were chosen
• Their information was sent to 8 physicians and MYCIN
• All then came up with recommended treatment
• Those recommendations along with what the patient
actually received as treatment was sent to 8
unidentified specialists
7. WHO WON FROM THE TEST
• MYCIN won the test
• MYCIN received the highest score as for as
accuracy and effectiveness of diagnosis
8. STAGES IN DAIGNOSIS AND
TREATMENT
• Decide if there is significant infection
• Identify the causing organism(s) by clinical and
laboratory evidence
• Decide what antibiotic agent the organisms are
sensitive to
• Prescribe the optimal drug combination for particular
case
9. RESULTS
Never implemented for routine clinical use
Shown to be competent by panels of experts even in
case where experts themselves disagreed on
conclusions
Key contributions
Reuse of production rules( expansion knowledge aquision )
10. PROBLEMS
• If the system gives an incorrect diagnosis, who is
to be blamed or sued
• Physicians rely to heavily on system instead of
consulting other experts
11. Resources
Wikipedia Page
Building Expert Systems in Prolog
Available at: http://www.amzi.com/ExpertSystemsInProlog/xsipfrtop.htm
Artificial Intelligence - A modern Approach by Stuart Rassell, Peter
Norvig
PC AI – Expert Systems
Available at: http://en.wikipedia.org/wiki/Expert_system
Available at: http://www.pcai.com/web/ai_info/expert_systems.html
VisiRule Web Demos (A simple example):
http://www.lpa.co.uk/vrs_dem.htm