Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures

TL;DR

This study demonstrates that fine-tuned transformer models can accurately extract anginal symptoms from physician notes, aiding in cardiovascular risk assessment and management.

Contribution

It introduces a domain-specific transformer approach for extracting anginal symptoms from clinical notes, showing high accuracy in symptom detection.

Findings

High sensitivity and specificity for chest pain detection

Effective extraction of shortness of breath and dyspnea

Promising method for NLP in clinical symptom characterization

Abstract

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.