MYCIN was an early expert system developed at Stanford University in 1972 to assist physicians in diagnosing and selecting treatment for bacterial and blood infections. It used over 600 production rules encoding the clinical decision criteria of infectious disease experts to diagnose patients based on reported symptoms and test results. While it could not replace human diagnosis due to computing limitations at the time, MYCIN demonstrated that expert knowledge could be represented computationally and established a foundation for more advanced machine learning and knowledge base systems.

1. MYCIN
Dr. C.V. Suresh Babu

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What is MYCIN?
 MYCIN, an early expert system, or artificial intelligence (AI) program,
designed to assist physicians in the diagnosis of and therapy selection for
treating patients with bacterial and blood infections.
 The system was actually not used in clinical practice, but it constitutes an
excellent early example of a digital expert system and a precursor to much
more sophisticated machine learning and knowledge base systems years later.

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When and Where?
 In 1972 work began on MYCIN at Stanford University in California

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The Name
 MYCIN is not an acronym.
 The name was chosen after attempts at finding a suitable acronym failed.
 The name is the common suffix associated with many antimicrobial agents.
 Examples of aminoglycosides include
 gentamicin,
 tobramycin,
 neomycin, and
 streptomycin

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How?
 MYCIN would attempt to diagnose patients based on reported symptoms and
medical test results.
 The program could request further information concerning the patient, as
well as suggest additional laboratory tests, to arrive at a probable diagnosis,
after which it would recommend a course of treatment.
 If requested, MYCIN would explain the reasoning that led to its diagnosis and
recommendation.

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Features
 In addition to the consultation system itself, MYCIN contains an explanation
system which can answer simple English questions in order to justify its advice
or educate the user.
 Much of MYCIN's power derives from the modular, highly stylized nature of
these decision rules, enabling the system to dissect its own reasoning and
allowing easy modification of the knowledge base.

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Production Rules
 The system's knowledge is encoded in the form of some 600 production rules
which embody the clinical decision criteria of infectious disease experts.
 Users would enter answers to a series of “yes” or “no” questions and short
answer questions, and the program would eventually choose a weighted
probability for a diagnosis.

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Limitations
 Part of the limitation of this early program was simply computing power –
because the program was estimated to take up to half an hour to get through
in a clinical environment, it was not considered effective enough to replace
human diagnosis at the time.
 Ethical questions also contributed to the decision not to use Mycin for clinical
diagnosis.

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Conclusion
 Mycin has proven to be a stepping stone to more modern systems and
described in a book on rule-based expert systems by B. G. Buchanan and E. H.
Shortliffe as “the granddaddy of them all” in terms of early artificial
intelligence for machine learning systems