  Anginal symptoms can connote increased cardiac risk and a need for change in
cardiovascular management. This study evaluated the potential to extract these
symptoms from physician notes using the Bidirectional Encoder from Transformers
language model fine-tuned on a domain-specific corpus. The history of present
illness section of 459 expert annotated primary care physician notes from
consecutive patients referred for cardiac testing without known atherosclerotic
cardiovascular disease were included. Notes were annotated for positive and
negative mentions of chest pain and shortness of breath characterization. The
results demonstrate high sensitivity and specificity for the detection of chest
pain or discomfort, substernal chest pain, shortness of breath, and dyspnea on
exertion. Small sample size limited extracting factors related to provocation
and palliation of chest pain. This study provides a promising starting point
for the natural language processing of physician notes to characterize
clinically actionable anginal symptoms.
