Latent Structural Discovery in Clinical Texts via Transformer-Based Embeddings and Token Graphs

Electrocardiogram reports are an important component of cardiovascular diagnostics, routinely generated in hospitals and clinical settings to monitor cardiac activity and guide medical decision-making. ECG reports often consist of structured waveform data accompanied by free-text interpretations written by clinicians. Although the waveform data can be analyzed using signal processing techniques, the unstructured text component contains rich, contextual insights into diagnoses, conditions, and patient-specific observations that are not easily captured by conventional methods. Extracting meaningful patterns from clinical narratives poses significant challenges. In this work, we present an unsupervised framework for exploring and analyzing ECG diagnostic reports using transformer-based language modeling and clustering techniques. We use the domain-specific language model BioBERT to encode text-based ECG reports into dense vector representations that capture the semantics of medical language. These embeddings are subsequently standardized and subjected to a series of clustering algorithms, including KMeans, hierarchical clustering, DBSCAN, and K-Medoids, to uncover latent groupings within the data.