We build personalized relevance parameterization method (prep-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 prep-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 for the results computed by prep-ad. These relatively small average loocv errors and p-values suggest that our prep-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.

中文翻译：

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基因表达，海马体减量和MMSE分数计算阿尔茨海默氏病的进展和药物治疗效果。

我们基于人工智能（ai）技术构建个性化的相关性参数化方法（prep-ad），以计算轻度认知障碍（mci）阶段患者的阿尔茨海默氏病（ad）进展。广告相关基因的表达，迷你型精神状态检查（mmse）得分以及mci患者的海马体积测量值可从阿尔茨海默氏病神经影像学倡议（adni）数据库中获得。在评估药物治疗下的认知变化时，将根据可用的临床测量和所用治疗类型（即多奈哌齐单药治疗和多奈哌齐与美金刚的多药治疗）对患者进行分组。如果使用mmse分数计算60个月的疾病进展，则预备广告的平均留一法交叉验证（loocv）错误率小于8％，而使用海马体积测量12个月时，平均留一法交叉错误率低于8％。对于在疾病进展中使用与广告相关的基因以及由prep-ad计算的结果，统计显着性计算为p = 0.003。这些相对较小的平均loocv误差和p值表明，我们采用基因表达，mmse评分和海马体积损失测量的pre-ad方法可在广告早期支持药物治疗决策时有用。003，将ad相关基因用于疾病进展，以及prep-ad计算的结果。这些相对较小的平均loocv误差和p值表明，我们采用基因表达，mmse评分和海马体积损失测量的pre-ad方法可在广告早期支持药物治疗决策时有用。003，将ad相关基因用于疾病进展，以及prep-ad计算的结果。这些相对较小的平均loocv误差和p值表明，我们采用基因表达，mmse评分和海马体积损失测量的pre-ad方法可在广告早期支持药物治疗决策时有用。