  Dementia is a neurodegenerative disorder that causes cognitive decline and
affects more than 50 million people worldwide. Dementia is under-diagnosed by
healthcare professionals - only one in four people who suffer from dementia are
diagnosed. Even when a diagnosis is made, it may not be entered as a structured
International Classification of Diseases (ICD) diagnosis code in a patient's
charts. Information relevant to cognitive impairment (CI) is often found within
electronic health records (EHR), but manual review of clinician notes by
experts is both time consuming and often prone to errors. Automated mining of
these notes presents an opportunity to label patients with cognitive impairment
in EHR data. We developed natural language processing (NLP) tools to identify
patients with cognitive impairment and demonstrate that linguistic context
enhances performance for the cognitive impairment classification task. We
fine-tuned our attention based deep learning model, which can learn from
complex language structures, and substantially improved accuracy (0.93)
relative to a baseline NLP model (0.84). Further, we show that deep learning
NLP can successfully identify dementia patients without dementia-related ICD
codes or medications.
