One Report, Multifaceted Views: Multi-Expert Rewriting for ECG Interpretation

Data scarcity is a significant barrier to developing high-performing AI models for medical text classification. To improve stroke prediction from electrocardiogram (ECG) interpretations reports where training data are scarce, we propose a novel data augmentation technique, Multi-Expert Perspective Augmentation. We use the LLM Phi-4 to rewrite original machine-generated ECG reports from the simulated perspectives of five different medical specialists. This prompt-based approach generates text with diverse clinical viewpoints while preserving the original meaning. We trained a BiomedBERT-based Gradient Boosting model to classify for stroke, comparing a baseline model trained only on the original data against a model trained with our augmented data. The model trained solely on original data showed poor performance (F1-score: 0.6698) with a severe precision–recall imbalance. In contrast, the augmented model achieved a significantly improved and balanced performance, with an F1-score of 0.8421, an accuracy of 0.8427, a precision of 0.8571, and a recall of 0.8276. Our method also outperformed other LLM-based augmentation techniques. Our findings demonstrate that rewriting text from multiple simulated expert perspectives is an effective strategy for data augmentation, enhancing the linguistic and contextual diversity of training data and leading to a more balanced and accurate classification model.