CS237 Intelligent Sytems

Expert Systems

Medical Expert Systems




Consultation system

Explanation System

Rule Acquisition system

Evaluation of Mycin

A Typical Consultation Session


Intelligent  systems, particularly expert  systems for diagnosis and treatment, have been developed for use in a range of medical contexts:

  • medical practitioners - hospital doctors, nurses, GPís, consultants, A & E depts, operating theatre, but also nursing home staff, sometimes parents, patients themselves
  • basic tasks - diagnosis, prognosis, treatment, monitoring
  • Challenges 

    People in the medical field have difficult decisions to make and have certain challenges to face:

    MYCIN: medical diagnosis using production rules  

    MYCIN was the first well known medical expert system developed by Shortliffe at Stanford University to help doctors, not expert in antimicrobial drugs, prescribe such drugs for blood infections. MYCIN has three sub-systems:


    Basic medical tasks when prescribing drugs for blood infections:
    1. is the patient suffering from an infection? (BUT bacteria occur normally; and samples can be contaminated)
    2. whatís the organism?
    3. which drugs are appropriate? (some drugs too toxic for safe use; no single anitbiotic is generally effective)
    4. which one(s) to select?
    5. lab tests - quickly for morphology, staining characteristics; later (24-48 hrs) for full identification; later still (1 - 2 days for sensitivity to antimicrobial agents)
    To consider when prescribing treatment:

    Problems that occur with drug prescription

    1. Mycinís Consultation System 

    - works out possible organisms and suggests treatments

    Rule base

    Example rule (in doctorese, pseudo-LISP):


    1) the stain of the organism is gramneg and

    2) the morphology of the organism is rod and

    3) the aerobicity of the organism is aerobic


    there is strongly suggestive evidence (0.8) that the class of the organism is enterobacteriaceae

    Static and dynamic data structures

    Static data structures

    These store medical knowledge not suitable for storage as inferential rules: includes lists of organisms, knowledge tables with features of bacteria types, list sof parameters

    Parameters = features of patients, bacterial cultures, drugsÖ

    Parameters can be Y/N (e.g. FEBRILE), single value (e.g. IDENTITY - if itís salmonella it canít be another organism as well) or multi-value (e.g. INFECT - patient can have more than one infection)

    Parameter properties

    EXPECT range of possible values

    PROMPT English sentence to elicit reponse
    LABDATA can be known for certain from Lab data
    LOOKAHEAD lists rules mentioning the parameter in their premise (e.g. a rule might need to know whether or not a patient is febrile)
    UPDATED-BY lists rules mentioning the parameter in their action (i.e. they may draw a conclusion about the value of the parameter, such as the IDENTITY parameter)

    Dynamic data structures store information about the evolving case - the patient details, possible diagnoses, rules consulted:

    Example piece of dynamic data:

    To  evaluate the premise of the rule mentioned above:




    from following data


    GRAM = (GRAMNEG 1.0)

    MORPH = (ROD 0.8) (COCCUS 0.2)

    AIR = (AEROBIC 0.6) (FACUL 0.4)

    Total value of whole expression is lowest certainty value = 0.6

    Conclusion is therefore:

    (CONCLUDE ORGANISM-1 is enterobacteriaceae 0.48 ( = 0.6 (CF of premise) x 0.8 (CF of rule)) (In disjunctive premise, total value of whole expression is highest certainty value)

    Control structure

    MYCIN first attempts to create a "patient context" containing information about the case, then tries to compile a list of therapies for the context.

    It uses a backward chaining mechanism, reasoning back friom the goals it want sto prove to the data it has, rather than vice versa. The overall goal is "compile a list of therapies".

    Questions can be prompted by the invocation of rules, to find out necessary data, to avoid unnecessary questions.

    2. The Explanation System 

    MYCIN can answer questions about HOW a conclusion was reached and WHY a question was asked, either after a consultation or while it is going on.

    It does this by manipulating its record of the rules it invoked, the goal it was trying to achieve, the information it was trying to discover.

    Can also answer general questions (e.g. what would you prescribe for organism X?) by consulting its static data structures.

    3. The Rule Acquisition System 

    Experts can enter new rules or edit existing rules. The system automatically adds the new rule to the LOOKAHEAD list for all parameters mentioned in its premise, and to the UPDATED-BY list of all paramters mentioned in its action.

    Evaluation of MYCIN 

    Initial evaluation: five experts agreed with 72% of its recommendations over 15 cases

    In later experiment, MYCINís performance was rated against senior and on-senior doctors:

    Source          Rating
    MYCIN 52
    Faculty 1 50
    Faculty 2 48
    Fellow 48
    Faculty 3  46
    Actual therapy  46
    Faculty 4 44
    Resident  36
    Faculty 5 34
    Student 24

    A Typical Consultation Session 

    1. Patient Information

    1) Patientís name


    2) Age

    34 years

    3) Sex



    6) Please enter results of pending cultures in table:


    CSF 1234 30.1 YES

    10) Do you suspect PT538 may have an infection at a site from which you have not obtained specimens?


    15) Has PT538 recently had objective evidence of abnormal neurologic signs (e.g. seizures, coma) documented by physician?


    The CSF culture will be considered to be associated with meningitis.


    17) Does PT538 have an abnormal chest x-ray?


    18) Has PT538 been exposed to any contagious diseases recently?


    27) Please enter CSF findings in table:

    WBC %PMN Protein CSFglucose Periph-glucose

    100 99 280 5 UNKNOWN

    35) Is meningitis a hospital-acquired infection?


    39) is PT538 a burns patient?


    I have found evidence (RULE545 RULE557)that treatment should cover for e-coli causing meningitis

    I have found evidence (RULE545 RULE557 RULE578)that treatment should cover for pseudomaonas aeruginosa causing meningitis

    I have found evidence (RULE545 RULE557 RULE578)that treatment should cover for Klebsiella pneumoniae causing meningitis

    I have found evidence (RULE507)that treatment should cover for Diplococcus-pneumoniae causing meningitis


    It is important to cover for the following infection(s) and associated organism(s):


    <item 1>e-coli

    <item 2>pseudomaonas aeruginosa

    <item 3>Klebsiella pneumoniae

    <item 4>Diplococcus-pneumoniae

    41) Does PT538 have a clinically significant allergic reaction to any antimicrobial agent?


    43) PT538ís weight in kilograms:


    Recommendation 1

    My preferred therapy is as follows:

    In order to cover for items <1 2 3 4>

    give the following in combination:


    Dose 3.5g q4h IV


    Dose 119 mg q8h IV

    Since high concentration of penicilllins can inactivate aminoglycosides do not mix these two antibiotics in the same IV bottle.

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