  The increasing demand for personalized health care has led to the expectation
that individualized quantitative evaluation of human disease states is
possible. However, this has not yet been achieved at a sufficiently low cost.
Our ultimate goal is to determine the most accurate distributions of blood
tests commonly used in health checkups. In this study, we quantified
differences between the estimated distributions based on four datasets using
the lognormal distribution with three parameters and analyzed the cause of the
differences. We focused on two causes of differences: the exclusion criteria
and distribution used for estimation of distributions. We compared the expected
values across datasets for each laboratory test. We also quantitatively
evaluated differences in the shape of the estimated distribution corresponding
to the exclusion criteria. We found that exclusion criteria have an important
influence on the shape of the distribution for blood test values.
