Computation of Pharmacologic Therapy Effects on Cognitive Abilities of Alzheimer’s Disease Patients

Abstract

To explore the impact of pharmacologic therapies on cognitive changes of Alzheimer's disease (AD) patients, we develop an artificial intelligence (AI) based personalized relevance parameterization method, called PReP-AD-PH. Expressions of genes, which are effective in AD related protein biomarkers, and mini mental state examination (MMSE) scores of AD patients in mild cognitive impairment (MCI) stage are inputs for PReP-AD-PH. In this study, AD patients in MCI stage are split into two groups, such that the first group has 81 patients given monotherapy with cholinesterase inhibitor (ChEI) donepezil and the second with 70 patients received combinational therapy with donepezil and memantine. PReP-AD-PH computes parameters characterizing the cognitive changes in AD patients with MCI. Using a leave-one-out-cross-validation (LOOCV) based algorithm, we measure an average LOOCV error rate of 6.53% for patients received donepezil monotherapy, and 8.05% for those under combinational therapy. Cumulative distribution of LOOCV error rates of PReP-AD-PH results points out that AI based computation methods can be useful in assisting clinicians with pharmacologic therapy decisions for AD patients with MCI.