Information Technology Reference
In-Depth Information
Dynamic Pertinence
In most of the situations the doctors will not give a static pertinence for a document
neither a patient. So we need to learn the pertinences. Hence we propose to do it
according to the accesses stored in the RADB database.
Due to the great number of records in the RADB database we need a very efficient
process. If we consider that we want the system to be dynamic and to update on-
line the pertinences according to the new accesses, the efficiency requirement is
even more important.
As we have mentioned, different methods to calculated the relevance of prefer-
ences can be found in literature but the great complexity that they have, makes them
not valid for our system. It has lead us to propose the new method explained next.
To calculate to pertinence we propose to use an adaptation of the Vector Space
Model [49]. This technique comes from the Documentary Computing, concretely
from the automatic indexation methods and retrieval systems [24]. It is used to deter-
mine which descriptors are more specific or discriminate better between documents.
The discrimination value classifies terms in the text according to their capability
to distinguish some documents from others in a given collection; i.e., the discrimi-
nation value of a term depends on how the average distance between the documents
changes when a content identification is set for the term. Therefore, the best words
are those resulting in a higher distance.
The basic idea of this model lays in the construction of a matrix or table of infor-
mation items and documents, where the rows are the terms and the columns corre-
spond to the documents acceded.
The rows would correspond to the terms that would be expressed according to
the occurrences (access frequency) of each information item.
Applying it to our case, we consider as documents (columns) the possible
Con-
texts
and as terms (rows) the
data groups
inside the documents. Hence, the table
with the access frequencies will be like the one show in Figure 23.5, where
tf
ij
represents the number of accesses to the
data group i
in the
context j
,and
N
i
=
1
tf
ij
tf
j
=
(23.2)
gives information about the total accesses for
context j
.
However in our situation it is not enough, since we need the recent accesses
have to a higher influence than older ones when calculating the pertinence. This
is why we propose to measure the relevance according to the time as the weight
function shown in Figure 23.6. Let
D
R
be a reference date to consider relevant or
not the information for the system and
D
A
represent the access date. In that case,
we propose the following function to calculate the weight for a given date (
D
A
):
2
D
A
−
D
R
W
(
D
A
)=
(23.3)
365
where the date difference is calculated in days. This way an access made today will
have more influence that the accesses of the last year but, as the time goes by, less
