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

Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning