聚类分析
乳腺癌
医学
比例危险模型
生存分析
层次聚类
数据挖掘
肿瘤科
癌症
人工智能
计算机科学
内科学
作者
Yuan Gu,Mingyue Wang,Yishu Gong,Xin Ying Li,Z. Wang,Yuli Wang,Jiang Song,Dan Zhang,Chen Li
出处
期刊:Future Oncology
[Future Medicine]
日期:2023-12-01
卷期号:19 (40): 2651-2667
被引量:1
标识
DOI:10.2217/fon-2023-0736
摘要
Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan-Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.
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