  Dementia is under-recognized in the community, under-diagnosed by healthcare
professionals, and under-coded in claims data. Information on cognitive
dysfunction, however, is often found in unstructured clinician notes within
medical records but manual review by experts is time consuming and often prone
to errors. Automated mining of these notes presents a potential opportunity to
label patients with cognitive concerns who could benefit from an evaluation or
be referred to specialist care. In order to identify patients with cognitive
concerns in electronic medical records, we applied natural language processing
(NLP) algorithms and compared model performance to a baseline model that used
structured diagnosis codes and medication data only. An attention-based deep
learning model outperformed the baseline model and other simpler models.
