Gene Expressions, Hippocampal Volume Loss, and MMSE Scores in Computation of Progression and Pharmacologic Therapy Effects for Alzheimer's Disease

We build personalized relevance parameterization method (
pr
e
p-ad
) based on artificial intelligence (
ai
) techniques to compute Alzheimer's disease (
ad
) progression for patients at the mild cognitive impairment (
mci
) stage. Expressions of
ad
related genes, mini mental state examination (
mmse
) scores, and hippocampal volume measurements of
mci
patients are obtained from the Alzheimer‘s Disease Neuroimaging Initiative (
adni
) database. In evaluation of cognitive changes under pharmacological therapies, patients are grouped based on available clinical measurements and the type of therapy administered, namely donepezil monotherapy and polytherapy of donepezil with memantine. Average leave one out cross validation (
loocv
) error rates are calculated for
pr
e
p-ad
results as less than 8 percent when
mmse
scores are used to compute disease progression for a 60 month period, and 3 percent with hippocampal volume measurements for 12 months. Statistical significance is calculated as
$p = 0.003$
for using
ad
related genes in disease progression and as
$p < 0.05$
for the results computed by
pr
e
p-ad
. These relatively small average
loocv
errors and
$p$
-values suggest that our
pr
e
p-ad
methods employing gene expressions,
mmse
scores and hippocampal volume loss measurements can be useful in supporting pharmacologic therapy decisions during early stages of
ad
.