NLP and machine learning identified six distinct symptom clusters in AF patients undergoing ablation, with the most frequent symptoms being dyspnoea (64%), oedema (62%), and palpitations (57%).
Cross-Sectional (n=1,293)
What are the distinct symptom clusters in patients with atrial fibrillation undergoing catheter ablation, and what are their clinical correlates?
NLP and machine learning can identify distinct symptom clusters in AF patients undergoing ablation, which correlate with specific demographic and clinical profiles.
OBJECTIVE: This study aims to leverage natural language processing (NLP) and machine learning clustering analyses to (1) identify co-occurring symptoms of patients undergoing catheter ablation for atrial fibrillation (AF) and (2) describe clinical and sociodemographic correlates of symptom clusters. METHODS: We conducted a cross-sectional retrospective analysis using electronic health records data. Adults who underwent AF ablation between 2010 and 2020 were included. Demographic, comorbidity and medication information was extracted using structured queries. Ten AF symptoms were extracted from unstructured clinical notes (n=13 416) using a validated NLP pipeline (F-score=0.81). We used the unsupervised machine learning approach known as Ward's hierarchical agglomerative clustering to characterise and identify subgroups of patients representing different clusters. Fisher's exact tests were used to investigate subgroup differences based on age, gender, race and heart failure (HF) status. RESULTS: A total of 1293 patients were included in our analysis (mean age 65.5 years, 35.2% female, 58% white). The most frequently documented symptoms were dyspnoea (64%), oedema (62%) and palpitations (57%). We identified six symptom clusters: generally symptomatic, dyspnoea and oedema, chest pain, anxiety, fatigue and palpitations, and asymptomatic (reference). The asymptomatic cluster had a significantly higher prevalence of male, white and comorbid HF patients. CONCLUSIONS: We applied NLP and machine learning to a large dataset to identify symptom clusters, which may signify latent biological underpinnings of symptom experiences and generate implications for clinical care. AF patients' symptom experiences vary widely. Given prior work showing that AF symptoms predict adverse outcomes, future work should investigate associations between symptom clusters and postablation outcomes.
Hobensack et al. (Tue,) conducted a cross-sectional in Atrial fibrillation (n=1,293). Natural language processing and machine learning clustering was evaluated on Symptom clusters. NLP and machine learning identified six distinct symptom clusters in AF patients undergoing ablation, with the most frequent symptoms being dyspnoea (64%), oedema (62%), and palpitations (57%).