Alzheimer's Disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. . However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. In this study, we developed a rule-based NLP algorithm and machine learning models to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the clinical notes of patients diagnosed with AD. We trained and validated the proposed models on the clinical notes retrieved from the University of Pittsburgh of Medical Center (UPMC). The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts.

本研究利用基于规则的自然语言处理（NLP）算法以及机器学习模型，自动从AD患者的临床笔记中提取包括打鼾、打盹、睡眠问题、舒适度、日间嗜睡、夜间醒来和睡眠时长等与睡眠有关的概念，结果表明该算法对所有睡眠概念都表现出最佳性能。

使用自然语言处理从阿尔茨海默病患者的临床笔记中提取睡眠信息