Article summary of Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project by Buchanan & Shortliffe - Chapter

The branch of computer science concerned with symbolic, (non)algorithmic methods for problem solving is called artificial intelligence (AI). An algorithm is a procedure that guarantees to find a fitting solution for a problem or that there is no solution. For many years, computers have been doing numerical calculations for data processing. However, knowledge about a certain subject is rarely numerical, but rather symbolical. Therefore, its problem-solving methods are rarely mathematical. It relies on heuristics, that are not guaranteed to work, but often find solutions quicker than trial-and-error. MYCIN is an expert system and designed to:

1. Provide expert level solutions for complex problems.

2. Being understandable.

3. Being flexible enough to easily accommodate new knowledge.

What is the context of the MYCIN system?

The MYCIN system exists from two parts. First, a knowledge base and second an inference mechanism. Sometimes there are subprograms added to facilitate interaction with users. There are two flows of information between the user and the system. The user either inputs information in the form of the description of a new case. The second flow is the output of advice and explanation by the expert system. These interactions all go through the user interface which then communicates to the system. The underlying knowledge base is the store of the program containing facts and associations about a subject. The inference mechanism or control structure can take many forms, for example a chained together set of rules about the facts and statements in the knowledge base. This is called forward chaining or data-directed inference because the data is known, by chaining a solution is provided. MYCIN however uses primarily backward chaining, a goal-directed control strategy. In this strategy, the system starts with an argument (the goal) and works backwards through inference rules to find the data that establishes that goal. Because of the many rule chains and data, the system should inquire, MYCIN is sometimes referred to as an evidence-gathering program. The goal of the work of the MYCIN is to provide diagnostic and therapeutic advice about a patient. The whole system is referred to as performance system as the other subsystems are not directly related to giving advice.

How was the programming language chosen?

For the creation of MYCIN, the LISP programming language was used. The reason is the extreme flexibility based on a small number of simple constructs. Furthermore, this language allows rapid testing and modification. This way, medical rules in the knowledge base were easily separated from inference procedures that use the rules. Also, LISP does not require recompilation of programs to test them. It removes the distinction between program and data, so parts of the program can be examined and edited as if they were data structures.  Currently with the release of Interlisp, additional tools such as EMYCIN have been built on top of Interlisp.

What is the historical perspective on MYCIN?

Production rules for artificial intelligence were probably brought up by Allen Newell. He saw an elegant formalism that was useful for psychological modelling. Production rules can be utilized to encode domain-specific knowledge. For example, Waterman (1970) worked with heuristics and rules of a poker game. This research led to the development of the DENDRAL program, the first AI program to emphasize the generalized problem-solving capabilities over specialized knowledge. The program was able to construct explanations of analytic data about molecular structures. A major concern here was the representation of specialized knowledge of the chemistry domain for the computer to use in problem solving. In a sense, MYCIN is an outgrowth of the DENDRAL program because the design and implementation are based upon it.

Before a system can make appropriate therapeutic decisions, it should contain all knowledge of antimicrobial selection. Also, by modifying the input from patient databases to direct data entry a consultation with physicians was realized. This made the system interactive. The model for MYCIN should be able to diagnose and suggest therapy. One difficulty was seeing how a long dialogue between therapist and patient could be focused on one line of reasoning at a time. Furthermore, translating the ill-structured knowledge of infectious disease into semantic networks remains difficult. In the first grant application of MYCIN, the goals of the project were described:

• A consultation program that provide physicians with advice regarding antimicrobial therapies. This is based on data on microbiology and direct observations of the physician.

• It should have interactive explanation capabilities to explain its knowledge of disease therapy and justify recommendations.

• It should contain computer acquisition of judgemental knowledge. The MYCIN system should be taught therapeutic decision rules, useful in clinical practice.

At the time of building MYCIN, researchers also worked on other subprojects in AI, such as question answering (QA), inference, explanation, evaluation and knowledge acquisition.

What about MYCIN’s task domain; antimicrobial selection?

The nature of the decision problem in MYCIN originates in the task domain: antimicrobial selection. An antimicrobial agent is a drug that is designed to kill bacteria or arrest growth. Selection of antimicrobial therapy is the problem of choosing an agent (or combination) to treat a patient with an infection. A naturally occurring bacteria or fungi is called antibiotic. Some are too toxic in treating infectious diseases. Besides antibiotics, antimicrobial or synthetic antibiotics can be used in treatment of infections. The cause of the infection is used as a clue for the decision what drugs are beneficial. The selection of therapy is done in four parts:

1. The physician decides whether the patient has a significant infection.

2. If this is the case, the organism causing the infection needs to be identified.

3. Select a set of drugs that might be appropriate.

4. Finally, the (combination of) drugs should be chose for treatment.

How can useful drugs be selected for the patient?

First, the isolation of bacteria from a patient is not evidence of a significant infection, they are often important to the homeostasis of the patients’ body. The second challenge is samples can be contaminated with external organisms. It is wise to use several samples. The significance is then based on clinical criteria. These allow the physician to judge the significance of the infection. Second, there are several laboratory tests that can check the causing organism of an infection. The complete test and definition of identity can take up to 24-48 hours. The problem with the process is that the patient cannot wait several days before therapy. Early data becomes important for narrowing down the possible identities. Historical information about the patient is also useful. Lastly, to discover the range of antimicrobial sensitivities of the organism, in vitro tests are run. The bacterium is exposed to several commonly used antimicrobial agents and the sensitivity is analysed. Then the physician knows what drugs can be effective in vivo (in the patient). This data is only available after several days; therefore, a decision often is based on statistical data available from hospital laboratories. Once a list with potentially useful drugs is created, the likelihood of its effect should be considered. The patient’s allergies, sex, age and kidney status should be examined, just like the administration of the drug. As the patient’s status can vary, so should the recommended dosage of the drug.

When came the evidence for needing assistance?

The use of sulfonimines and penicillin cannot be overstated. In the 1950s it became clear that antibiotics were misused. At the time of developing MYCIN this misuse was receiving a lot of attention. Many antibiotics were prescribed without the identification of the offensive organism. Antibiotics is overprescribed because the patient demands a prescription with every visit. Improved public education is one step towards diminishing the problem. Studies have shown that one-third of hospitalized patients receive a kind of antibiotics. The monetary cost is enormous. This issue is summarized by Simmons and Stolley in 1974.

1. Have new resistant bacterial strains have emerged because of the wide use of antibiotics?

2. Has the hospital ecology changed because of this use?

3. Have infections changed because of antibiotics misuse?

4. What are trends in the use of antibiotics?

5. Is there a proper use of antibiotics?

6. Is the more frequent use of antibiotics presenting new hazards?

The answers to the questions are frightening to an extent that the consequences might be worse than the disease. This raises a new question: are physicians basing their prescription habits on rational decisions? In a study of Roberts and Visconti (1972) it turns out only 13% was judged to be rational. The goal of MYCIN is to provide an improved therapy decision. An automated consultation system could support in a partial solution to the therapy selection problem.