Abstract 15133: Topic Models Using Notes From Electronic Medical Records Can Classify Non ST Elevation Myocardial Infarction Patients Based on the Presence of an Occluded Culprit Artery

Introduction: Approximately 33% of patients with a Non-ST-Elevation Myocardial Infarction (NSTEMI) have an occluded culprit artery which doubles their odds of adverse outcomes. Earlier identification of NSTEMI patients with occluded culprit artery may reduce their odds of adverse outcomes. Notes from the electronic health record (EHR) are rich sources of text data about signs and symptoms. Organizing text data into topics may help providers identify patients with NSTEMI and an occluded culprit artery sooner. Latent Dirichlet Allocation uses a deep-learning network to generate topics that can classify patients.

Methods: Notes transcribed during the first 72 hours of hospitalization were extracted from the EHR among NSTEMI patients who presented to a medical center between 2015-2020. The data was preprocessed by removing punctuation, numbers, and stop words. We decided 5 topics based on computed coherence value. Two authors determined whether the first 100 terms were associated with an occluded artery. Analyses were completed in R using packages topicmodels and tidyverse .

Results: We examined 45,025 notes from 2,223 patients (age=69

14 years old; 57% n=1267 male; 83% n=1,845 White) who experienced 2,685 hospitalizations (17%, n=462 NSTEMI re-hospitalizations). In Topic 1, 70% of the terms were associated with an occluded culprit artery including “chest pain” and “troponin”. In contrast, in Topic 5, 90% of terms were associated with an occluded culprit artery such as “edema” and “pulmonary”. Figure 1 shows the frequency of terms associated with an occluded artery per topic.

Conclusions: Topic 1 is most associated with an occluded culprit artery. Topic 5 is the least associated with an occluded culprit artery, instead associated with heart failure. Our results demonstrate it is feasible to develop topics to differentiate NSTEMI with and without an occluded culprit artery. Future research aims to identify patterns between signs and symptoms within topics.