用UMAP降维+DBSCAN聚类，发现乳腺癌电子病历中的潜在分组。
An unsupervised clustering analysis of breast cancer data derived from electronic health records enhanced through UMAP dimensionality reduction
- 先用UMAP降维，再用DBSCAN聚类电子病历数据。
- 三种评估指标显示组合方法显著提升聚类效果。
- 适合医学数据分析、精准医疗研究者参考。
乳腺癌是全球约800万女性罹患的最常见癌症类型。患者电子健康记录可作为计算分析的宝贵数据集，揭示病理新见解。无监督聚类能识别具有医学意义特征的患者群，发现医生可能忽略的数据趋势。本研究对三个独立的乳腺癌电子病历数据集应用基于密度的DBSCAN聚类方法，并在聚类前通过UMAP进行降维处理以增强结果。使用三种统计指标（DBCV、DCSI和DISCO）评估聚类效果。结果证实，将UMAP与DBSCAN结合在电子健康记录数据聚类中具有显著有效性，为所识别患者群体的医学解释提供了可行路径。
原文摘要 · Abstract (English)
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.
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