对心血管疾病的无监督机器学习:未来研究的框架
Emmanuel Bresso1, Claire Lacomblez1, Kévin Duarte1
1Université de Lorraine, INSERM, Centre d'Investigations Cliniques Plurithématique 1433, Inserm U1116, CHRU de Nancy and F-CRIN INI-CRCT, Nancy, France.
European journal of heart failure
|November 6, 2025
概括
使用集群算法,无监督的机器学习可以识别心血管疾病 (CVD) 的不同患者子组. 该框架有助于开发有效的模型,以改善风险分层和个性化治疗策略.
科学领域:
- 心脏病学 心脏病学
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
背景情况:
- 心血管疾病 (CVD) 的管理需要精确的患者分层.
- 无监督机器学习 (ML) 提供了基于临床数据模式识别患者子组的潜力.
- 目前对心血管疾病的集群方法的临床应用受到缺乏可访问的工具和验证的阻碍.
研究的目的:
- 在心血管疾病研究中应用无监督ML集群技术的系统框架.
- 引导开发可靠的,外部验证的临床整合模型.
- 增强聚类的实用性,以改善心血管疾病风险分层和个性化治疗.
主要方法:
- 审查无监督机器学习技术,特别是集群算法.
- 为患者集群识别和验证制定一个阶段性框架.
- 结合结果关联和临床应用的预测建模.
主要成果:
- 提出了一个系统的框架,用于将无监督的ML应用于心血管疾病研究.
- 该框架有助于识别具有不同预后的不同患者群.
- 它支持开发用于临床使用的外部验证的预测模型.
结论:
- 无监督的ML集群可以显著改善心血管疾病患者的表征和分层.
- 拟议的框架为开发临床适用,验证的模型提供了路线图.
- 这种方法有望提高风险分层,并为心血管疾病患者个性化治疗策略.
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