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information about R SD and in that case we may even have R SD =
R SD =somelow
value (including 0 if there is no information at all about a given disease).
Notice, that the exclusion relation R SD was introduced in [8] to define Conorm-
Cadiag to be able to establish a correspondence between CADIAG and MYCIN-like
systems. CADIAG-like and MYCIN-like are computer assisted medical diagnosis
systems for different applications. CADIAG-like systems are based on fuzzy rules
and an inference procedure - a composition of fuzzy relations - is applied. MYCIN-
like systems use combining functions to calculate the global weights (degrees) of
suggested diagnoses [8, 12].
We intend here to show different types of S
D relations, given by an expert
as initial information, and we do not discuss a possibility to substitude R SD
by, for
R SD .
We may introduce another type of relation R t SD (
example, 1
- temporal or timeofmani-
festation. This relation shows to which degree it is true that exhibition of a symptom
s leads to immediate manifestation of a disease d (or a disease d is manifested after
some period of time). Notice, that this relation may be also estimated by linguistic
values from Table 25.1.
Thus, it can be seen that relations between symptoms/signs and diseases can
be of different type (occurrence, confirmation, exclusion, temporal, etc.). And in
general, it depends what information is available, what estimations are given by
expert-physicians.
s i ,
d j )
25.3.2
The Patient Information
To use an approximate reasoning mechanism to infer a diagnosis, information about
a patient to whom a diagnosis will be established has to be available. Although med-
ical knowledge concerning S
D relationship constitutes one source of imprecision
and uncertainty in the diagnostic process, the knowledge concerning the state of the
patient constitutes another [16].
Information about patients' symptoms/signs is presented in the form of a fuzzy
set S p :
, where each element of the fuzzy set S p shows to which degree it
is true, that a patient p has symptom/sign s i , or a degree of possibility of the presence
of the symptom, or its severity; symbol D p is used for the final diagnosis for a patient
p , D p :
Σ [
0
,
1
]
, where each element of the fuzzy set D p shows to which degree it
is true, that a patient p has a given disease d j , or a degree of possibility with which
we can attach each relevant diagnostic label to the patient [16].
In the Section 2 we have described criteria that are used for diseases description.
Due to our denotations, these criteria are used for construction of a knowledge-base
of type, e.g., R SD , R SD , R SD , R t SD and also these criteria represent symptoms/signs of
a patient to be estimated. Notice, that in our approach we use linguistic scale (Table
25.1) and its simple numerical representation (Table 25.3) for S
Δ [
0
,
1
]
D relations and,
correspondingly, the same estimations (linguistic and numerical) are assumed for a
degree of truth, that a patient p has symptom s i '. Additionally note, that, although
 
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