通过UMAP降维增强的电子健康记录中乳腺癌数据的无监督聚类分析
An unsupervised clustering analysis of breast cancer data derived from electronic health records enhanced through UMAP dimensionality reduction
浏览论文内容
中文总结 AI 辅助
研究利用电子健康记录中的乳腺癌数据，先采用DBSCAN密度聚类法，又通过UMAP降维增强效果，用三个统计指标评估聚类结果，证实了UMAP与DBSCAN结合用于该数据聚类的有效性，为医学解读患者组提供了支持。
中文摘要 AI 辅助
乳腺癌是最常见的癌症类型之一，全球约800万女性受其影响。被诊断患有这种疾病的患者的电子健康记录可作为有价值的计算分析数据集，有助于发现有关病理学的新见解。无监督聚类可识别具有医学重要特征的患者组，揭示医生可能忽略的数据趋势。本研究首先将基于密度的DBSCAN聚类方法应用于来自乳腺癌患者电子病历的三个独立数据集。随后，为增强结果，在应用DBSCAN之前使用UMAP进行降维阶段。使用三个统计指标（DBCV、DCSI和DISCO）评估聚类结果。结果证实了将UMAP与DBSCAN结合用于电子健康记录数据聚类的有效性，为医学解释通过该方法识别的患者组铺平了道路。
英文摘要
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.
发表机构
- Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca（信息系统与通信系，米兰比可卡大学）
- Institute of Health Policy Management and Evaluation, University of Toronto（卫生政策管理与评估研究所，多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。
