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

Extraction of Sleep Information from Clinical Notes of Alzheimer's Disease Patients Using Natural Language Processing