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).
MYCIN is an expert system comprised of two major components:
| IF the infection is primary-bacteremia | ||
| AND the site of the culture is one of the sterile sites | ||
| AND the suspected portal of entry is the gastrointestinal tract | ||
| THEN there is suggestive evidence (0.7) that the infection is bacteriod. |
Humans interface with MYCIN by answering a series of diagnostic questions akin to what a physician may ask a patient, as well as prompting for relevant test results. MYCIN takes this data as input and either arrives at a set of answers with respective probabilities, or branches to other questions in order to narro it's search. Researchers at Stanford found MYCIN to have an approximate correctness rate of 65%, which is better than the majority of physicians who are not specialists in diagnosing infections, and only slightly worse than physicians who were experts in that field (who had an average correctness of approximately 80%).
MYCIN's knowledge base is small relative to those used by most rules-based systems today; it is on the order of ~500 rules. The science of the generation of these rules is known as "knowledge engineering". MYCIN uses a modification of the method of reasoning called "backward chaning" to search it's knowledge base. The modification comes in when the system is in the beginning stages of diagnosis, when the system asks a series of broad questions in order to weed out any unnecessary searches later on, such as checking for pregnancy if the patient is male. Only when the problem becomes more defined does MYCIN use full backwards chaining. If MYCIN checks a rule with a probability of 0.2 or less, it will abandon further searching on that particular rule.
The core of MYCIN, its inference engine, is called EMYCIN ("Essential MYCIN"). EMYCIN is the framework for MYCIN, a semi-separate system which could be used to create other rules-based expert systems to face problems similar to what MYCIN faces. In some cases this can be affected merely by changing the knowledge base.
If a computer can diagnose patients with a better rate of success than an average family doctor, why not implement it on a large scale? There are many problems associated with the implementation of an clinical expert system like MYCIN. The greatest of which is that it has never actually been used in a non-experimental way, i.e. it has never been actually used to diagnose and prescribe treatment for a patient. The reasons for this are twofold: