Alzheimer's disease (AD)-related global healthcare cost is estimated to be $1 trillion by 2050. Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare. Clinical research indicates that the onset and progression of Alzheimer's disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline. In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer's disease. We demonstrate this approach on 10 year's (1980 to 1989) of President Ronald Reagan's speech data set. Key linguistic biomarkers that indicate early-stage AD are identified. Experimental results show that Reagan had early onset of Alzheimer's sometime between 1983 and 1987. This finding is corroborated by prior work that analyzed his interviews using a statistical technique. The proposed technique also identifies the exact speeches that reflect linguistic biomarkers for early stage AD.

中文翻译：

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个性化早期阿尔茨海默氏病检测：以里根总统的演说为例

到2050年，与阿尔茨海默氏病（AD）相关的全球医疗保健费用估计为1万亿美元。但是，临床研究表明，早期诊断和干预有助于延长生活质量，并为个性化心理保健提供信息技术。临床研究表明，阿尔茨海默氏病的发作和发展会导致痴呆和其他心理健康问题。结果，患者的语言能力开始下降。在本文中，我们表明基于机器学习的无监督聚类和语言生物标记异常检测是用于阿尔茨海默氏病直观可视化和个性化早期检测的有前途的方法。我们在罗纳德·里根（Ronald Reagan）总统任职10年（1980年至1989年）时证明了这种方法 的语音数据集。确定了指示早期AD的主要语言生物标记。实验结果表明，里根（Reagan）在1983年至1987年之间早些时候就开始了阿尔茨海默氏病。这一发现得到了以前的工作的证实，该工作使用统计技术对他的访谈进行了分析。所提出的技术还可以识别出反映早期AD语言生物标志物的准确语音。