识别数字双胞胎以指导可解释的AI用于心力衰竭的诊断和预后
Feng Gu1,2, Andrew J Meyer1,3, Filip Ježek1
1Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, 48105, USA.
NPJ digital medicine
|February 18, 2025
概括
心血管系统的数字双胞胎提供了个性化的心力衰竭 (HF) 见解. 这种方法可以增强人工智能模型,以便更好地预测HF和个性化治疗策略.
科学领域:
- 心血管医学 心血管医学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 心力衰竭 (HF) 是一种复杂,异质的疾病,需要个性化治疗.
- 目前的临床数据综合方法不足以准确管理HF.
研究的目的:
- 开发和验证一个使用计算数字双胞胎的框架,以实现个性化的HF患者代表性.
- 在数字双胞胎数据上使用机器学习识别现象组和心血管死亡风险驱动因素.
- 通过整合数字双胞胎衍生的功能来改进HF的预后AI模型.
主要方法:
- 创建了心血管系统的机械计算模型,为343名HF患者生成数字双胞胎.
- 在数字双胞胎特征上利用无监督机器学习来识别现象组和风险驱动因素.
- 将数字双胞胎功能集成到人工智能 (AI) 预测模型中.
主要成果:
- 数字双胞胎提供了个体HF患者疾病状态的全面表示.
- 机器学习确定了可解释的现象组和心血管死亡风险的机制驱动因素.
- 结合数字双胞胎特性的人工智能模型显示,性能,可转移性和可解释性比单独临床变量更好.
结论:
- 数字双胞胎框架为理解个体高频异质性提供了一种新的方法.
- 这种方法提高了心力衰竭预后AI模型的准确性和可解释性.
- 该框架具有指导个性化治疗和改善HF患者治疗结果的巨大潜力.
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