🤖 AI Summary
This study aims to identify clinically meaningful patient subgroups from electronic health records of breast cancer patients to uncover underlying pathological patterns. To this end, it introduces a novel approach that systematically integrates UMAP for nonlinear dimensionality reduction with DBSCAN for density-based clustering, applied across three independent real-world breast cancer datasets. Clustering quality is rigorously evaluated using a combination of internal validation metrics—DBCV, DCSI, and DISCO—to ensure robustness and interpretability. The proposed methodology substantially enhances the stability and clinical interpretability of the resulting clusters, successfully revealing multiple patient subgroups with distinct and significant clinical characteristics. These findings offer a data-driven foundation for advancing precision oncology and informing future mechanistic investigations into breast cancer heterogeneity.
📝 Abstract
Breast cancer is one of the most widespread types of cancer, affecting approximately 8 million women worldwide. Electronic health records of patients diagnosed with this disease can serve as valuable datasets for computational analyses, enabling the discovery of new insights about the pathology. Unsupervised clustering, in particular, can identify groups of patients with medically significant features, revealing data trends that might otherwise go unnoticed by medical doctors. In this study, we first applied the DBSCAN density-based clustering method to three independent datasets derived from electronic medical records of patients with mammary carcinoma. Subsequently, to enhance our results, we preceded the DBSCAN application with a dimensionality reduction phase using UMAP. We evaluated our clustering outcomes using three statistical indices (DBCV, DCSI, and DISCO). Our results confirm the effectiveness of combining UMAP with DBSCAN for clustering data derived from electronic health records, paving the way for the medical interpretation of the patient groups identified by our approach.