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构建胃癌临床患者相似性网络
Rukui Zhang1, Zhaorui Liu2, Chaoyu Zhu3
1Institute of Biomedical Sciences, Fudan University, 131 Dongan Road, Shanghai 200032, China.
Bioengineering (Basel, Switzerland)
|August 29, 2024
概括
这项研究引入了一种新的人工智能 (AI) 模型,用于分析癌症患者数据,以确定类似的患者群体. 人工智能模型,癌症特定的临床患者相似性网络 (cPSN),通过揭示具有不同预后的不同患者子组来改善医疗保健管理.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 医疗信息学医学信息学
背景情况:
- 从分子测试和电子健康记录中获得的临床数据的数量不断增加,需要针对个性化医学进行先进的大数据分析.
- 现有的数据处理方法通常依赖于专家标签,限制了可扩展性和客观性.
研究的目的:
- 开发和验证一个大数据分析模型,使用人工智能 (AI) 来识别癌症队列中的类似患者.
- 证明AI在处理复杂的医疗数据中的临床实用性,以改善医疗管理.
主要方法:
- 使用一次性编码来实现统一的临床数据处理.
- 采用无监督学习和欧几里德距离来构建一个高维临床患者相似性网络 (cPSN).
- 通过整体生存率 (OS) 分析评估临床有效性和相关性.
主要成果:
- 使用胃癌数据成功构建了一个癌症特异的cPSN.
- 确定了不同的患者子组 (集群),在总生存期 (OS) 中存在显著差异,其中Cluster_2显示最佳,Cluster_5显示最差的预后.
- 临床特征分析显示,Cluster_2患者患胃癌的患病率较高.
结论:
- 开发的cPSN模型提供了卓越的解释性和临床意义,用于回顾现实世界患者的相似性.
- cPSN模型是可扩展的,可通用的,在各种数据类型中有效的,为癌症研究和管理提供了强大的工具.
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