Rule Acquisition systemEvaluation 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
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)
EXPECT range of possible values
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:
($AND (SAME ORGANISM-1 GRAM GRAMNEG)
(SAME ORGANISM-1 MORPH ROD)
(SAME ORGANISM-1 AIR AEROBIC))
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:
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.
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:
Typical Consultation Session
1. Patient Information
1) Patientís name
6) Please enter results of pending cultures in table:
SITE CULTURE# DATE EXAMINED
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 2>pseudomaonas aeruginosa
<item 3>Klebsiella pneumoniae
41) Does PT538 have a clinically significant allergic reaction to any antimicrobial agent?
43) PT538ís weight in kilograms:
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.