• Home   /  
  • Archive by category "1"

Prospector Expert System Case Study Ppt Examples


MYCIN is the name of a decision support system developed by Stanford 
University in the early- to mid-seventies, built to assist physicians in the
diagnosis of infectious diseases. The system (also known as an "expert system")
would ask a series of questions designed to emulate the thinking of an expert in
the field of infectious disease (hence the "expert-"), and from the responses to
these questions give a list of possible diagnoses, with probability, as well as
recommend treatment (hence the "decision support-"). The name "MYCIN" actually comes from antibiotics, many of which have the suffix "-mycin".

MYCIN was originally
developed by Edward Shortliffe for Stanford Medical School in the early-and mid-1970's. Written in Lisp, a language (a set of languages, actually) geared towards artificial intelligence, MYCIN was one of the pioneering
expert systems, and was the first such system implemented for the medical
field. The Goal of MYCIN was to compete in an experiment conducted at
Stanford Medical similar to the Turing Test. The case histories of ten patients
with different types of meningitis were submitted to MYCIN as well as to eight human physicians, including a resident, a research fellow, and five faculty specialists in infectious disease. Both MYCIN and the human physicians were given the same information. Both MYCIN's and the human physician's
recommendations (as well as a record of the treatment actually received by the patients) were sent to eight non-Stanford specialists, completely unidentified as to which recommendation was MYCIN's and which
were authored by the physicians. The outside specialists gave MYCIN the
highest score as far as accuracy of diagnosis and effectiveness of treatment.The framework for MYCIN
was derived from an earlier expert system called DENDRAL, created to find new chemical comounds in the field
of orgainic chemistry (also developed at Stanford).

Edward Shortliffe (1972)
Department of Medicine and Computer Science
Heuristic Programming Project
Stanford University School of Medicine, California

Logical Layout of MYCIN

http://www.computing.surrey.ac.uk/ai/PROFILE/mycin.html#Expert  University of Surrey
http://www.cs.colostate.edu/~howe/EMAI/ch3/node5.html Control Conditions in MYCIN: A Case Study, Colorado State University


DENDRAL is a program that analyses organic compounds to determine their structure. It is one of the early example of a successful AI program . it uses a strategy called plan-generate-test in which a planning process that used constraint-satisfaction techniques, creates lists of recommended and contraindicated substructures.


Mycin is a program that diagnoses infectious diseases. It reasons backward from its goal of determining the cause of a patient illness. It attempts to solve its goal of recommending a therapy for a particular patient by first finding the cause of the patient’s illness. It uses its production rule4s to reason backward from goals to clinical observations. To solve the top-level diagnostic goal, it looks for rules whose right sides suggest diseases. It then uses the left sides of those rules (the preconditions) to set up sub goals whose success would enable the rules to be invoked . these sub goals are again matched against rules, and their preconditions are used to set up additional sub goals.

Mycin is a well known rule based deduction system. Its expertise lies in the domain of bacterial Infections. Physicians usually must begin antibiotic treatment for patient who have bacterial infections without knowledge exactly which organism is the culprit. There is no time to wait for definitive laboratory culture evidence, which accumulates too slowly. For the desperately sick, therapy must begin at once – not 2 days from can either prescribe a broad – spectrum drug that covers all possibilities , or she can prescribed a better, disease – specific drug.

Mycin helps the physician to prescribe disease – specific drugs. Mycin in-forms it self about particular cases by requesting information from the physician about a patient’s symptoms, general condition. History, and laboratory – test results that can be obtained easily and quickly. At each point, the question mycin asks is determined by Mycin’s current hypothesis and the answers to all previous questions. Thus, the questions start as though taken from a checklist, but the questions then vary as evidence builds. Here is an except from a sample sessions:

What is the patient’s Name ?

john Doe.

- Male or female?


- Age ?

He is 55.

- Have you obtained positive cultures indicating general type ?


- What type of inflection is it ?

Primary bacteremia

- When did symptoms first appear ?

May 5

- Let’s call the most positive culture CI. From what site was CI taken ?

from the blood.

- When ?

May 9

- Let’s call the first significant organism from this culture UI. Do you know identify of UI?


- Is UI a rod or a coccus or something else ?


- What is the gram stain of UI?

Gram negative.

- Have there been positive culture from sites that are not associated with the

inflections about which you are seeking advice?


As we proceed through the processing stages of compute vision. We will no doubt be impressed by the similarities and parallel one can draw between vision processing and natural language processing . The - sensor stage in vision corresponds to speech recognization language understanding, the low and intermediate processing levels of vision correspond to syntactic and semantic language processing respectively, and high level processing, in both cases corresponds to the process of building and interpreting high level knowledge structures.

One thought on “Prospector Expert System Case Study Ppt Examples

Leave a comment

L'indirizzo email non verrĂ  pubblicato. I campi obbligatori sono contrassegnati *