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

机译:药物治疗对阿尔茨海默氏病患者认知能力的影响计算

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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.
机译:为了探索药物治疗对阿尔茨海默氏病(AD)患者认知变化的影响,我们开发了一种基于人工智能(AI)的个性化相关参数化方法,称为PReP-AD-PH。 PReP-AD-PH的输入是在AD相关蛋白生物标记物中有效的基因表达以及处于轻度认知障碍(MCI)阶段的AD患者的轻度精神状态检查(MMSE)评分。在这项研究中,MCI期的AD患者分为两组,第一组有81例接受胆碱酯酶抑制剂(ChEI)多奈哌齐的单药治疗,第二组有70例接受多奈哌齐和美金刚的联合治疗。 PReP-AD-PH计算表征MCI AD患者认知变化的参数。使用基于留一法交叉验证(LOOCV)的算法,我们测量接受多奈哌齐单药治疗的患者的平均LOOCV错误率为6.53%,接受联合治疗的患者的平均LOOCV错误率为8.05%。 PReP-AD-PH结果的LOOCV错误率的累积分布指出,基于AI的计算方法可帮助临床医生对MCI的AD患者进行药物治疗决策。

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