Information Technology Reference
In-Depth Information
environmental information acquisition service (
Envir_Info_Acqui
),
subjective feeling acquisition service (
Subj_Feel_Acqui
), and electronic
medical records service (
E_M_R
). They are not marked to be mutually
exclusive to each other, and hence can be executed in parallel.
Physi_
Info_Acquir
service can acquire some general physiological signals
such as body temperature, blood pressure, saturation of blood oxygen,
electrocardiogram, and some special physiological signals for cerebral
apoplexy, such as cerebral blood flow perfusion information, brain
wave, and blood vessel elasticity information.
Envir_Info_Acquir
ser-
vice can acquire temperature, humidity, air pressure, and other environ-
mental information.
Subj_Feel_Acquir
service can acquire the user's
subjective feelings, food intake, and so on, where the information is
often provided by the patient from the terminal.
E_M_R
service can
read the patient's medical history information. Based on such informa-
tion, the cerebral apoplexy-oriented data analysis and diagnosis service
(
Cere_Apop_Analy_Diagno
) can analyze the data according to a variety
of models, like an analysis model of blood vessel elasticity. Combining
the analytic results with EMR information of the patient, the personal
health risk assessment service (
Health_Risk_Assess
) assesses the
patient's risk level (RL). There are three cases of results: RL
¼
0or
in normal condition; RL
¼
1 or having some signs of disease; and
RL
¼
2 or ill. According to different risk levels, there are different
processes as determined by domain preferences and practical needs.
When RL
0, the personal health guidance service (
Health_Guid
)is
executed next to provide some suggestions and other health guidance.
When RL
¼
1, warn the patient first by the personal health real-time
warning service (
Real_Time_Warn
), and then enforce all-round
intervention and instruct the patient to deal with the case by means of
the all-round intervention service (
All_Round_Interven
). When RL
¼
2,
alarm his hospital and doctor directly by the emergency alarm service
(
Emerg_Alarm
), acquire the patient's location information by the
geographic information service (
Geograph_Info
), and then activate
emergency service (
Activ_Emerg
).
Through this service composition example, it is clear that WSC
techniques supporting both branch and parallel structures are required
for successful and efficient composition of services. It should be noted
that the use of artificial intelligence-based planning methods would not
be able to generate the choice structure in the lower part of Figure 6.2;
and the traditional technique would generate 24 solutions, each of
¼
