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Needless to say, the shape of Life Illness Curves and Life Quality Curves are not
only dependent on the perception of the patient, physician or society, but also from
the family environment, social class, economic scenario, politics, etc.
20.4
Conclusions
Through this work, we have explored the concepts of probability and fuzziness in
medicine and how they are based on the information available at hand. Perceptions
play a pivotal role in understanding and interpreting the world and every individual
has his or her own particular and subjective vision on patient complaints, symptoms,
states of the organism, diseases, therapies and recoveries. In order to battle this sub-
jectivity, physicians use numerical indicators such as biochemical laboratory values
and endless raw data from many types of clinical tests. However, and as we have
seen, many of the data obtained from a patient are fuzzy in nature. Since the clas-
sical mission of a physician is to interpret the available information and to relate a
set of symptoms with a precise disease or condition and then to find a suitable treat-
ment, we can realize that physicians live in a world composed by rules, knowledge,
information and experience where no sharp partitions do exist.
In this context, Life Illness Curves and Life Quality Curves are graphic repre-
sentations of a perception of something that already exist (i.e., the health evolution
of a patient through his or her life), an interpretation of reality in strong contrast to
subjective probability, a perception or personal judgement of something unknown
with respect to its future outcome. From this perspective it doesn't matter that LIC
and LQC curves are heuristic and fuzzy in nature. Every LIC/LQC curve is unique
and shows that the concept of “patient” entails a larger set of information that the
concept of “illness”. Under this perspective, we can even suggest that instead of
speaking about diseases, it's usually better to speak about patients. With this vision
in mind, we ultimately can realize that the mission of a physician is to diminish the
μ I (
x
) ,
t values in LIC and to raise the
μ L (
x
)
values in the family of LQC.
Acknowledgement. I would like to thank my wife, Dr. (MD) Ana Fernandez-Andrés, for
her comments and suggestions in the development of this work.
References
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Hauser, S., Josephson, S. (eds.) Harrison's Neurology in Clinical Medicine. McGraw-
Hill, Asia (2010)
3. Canavos, G.C.: Applied Probability and Statistical Methods. Brown and Company, Little
(1984)
4. Guyton, A.C., Hall, J.E.: Textbook of Medical Physiology. W. B Saunders Company,
Toronto (1996)
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