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开发一种深度学习模型来预测心力衰竭中的存活率:竞争风险和脆弱模型
Solmaz Norouzi1,2,3, Hossein Khormaei4, Mohammad Asghari Jafarabadi5,6
1Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Scientific reports
|September 30, 2025
概括
一个新的深度学习模型,深度神经脆弱性竞争风险 (DNFCR),整合了心力衰竭死亡率预测的脆弱性和竞争风险. 虽然显示出潜力,但其临床优越性比传统模型需要进一步验证.
科学领域:
- 心脏病学 心脏病学
- 生物统计学 生物统计学
- 人工智能的人工智能
背景情况:
- 心力衰竭 (HF) 死亡率预测是复杂的,通常涉及未观察到的患者异质性 (脆弱性) 和多种潜在的死亡原因 (竞争风险).
- 现有的深度学习 (DL) 模型要么解决脆弱性,要么解决竞争风险,但不能同时解决两者.
- 准确预测特定原因死亡率和处理被审查的数据仍然是医疗保健分析中的挑战.
研究的目的:
- 引入深度神经脆弱性竞争风险 (DNFCR) 模型,这是一个新的DL框架,将脆弱性和竞争风险集成为HF死亡率预测.
- 评估DNFCR的性能与使用真实世界HF患者数据的既定生存模型对比.
- 评估整合比例和非比例脆弱结构对预测准确性的影响.
主要方法:
- 对435名心力衰竭患者进行了回顾性队列研究,其中包括57个特征,并进行了5年的随访.
- 开发了新的深度神经脆弱性竞争风险 (DNFCR) 模型,包括脆弱性和竞争风险.
- 与DeepSurv和CoxPH模型相比,使用C指数,综合障碍得分 (IBS) 和整合损失净收益 (INBLL) 评估了性能.
主要成果:
- 在DNFCR模型中,在结合虚弱性时,在预测心力衰竭死亡率方面表现出边际但一致的改善 (C指数~0.66).
- 与其他特定原因死亡率模型相比,DNFCR-PF在IBS和INBLL中表现更好.
- 深度学习模型的准确性与传统模型相比较,显示了由数据和混因素影响的上下文依赖的优越性.
结论:
- DNFCR模型对心力衰竭和潜在的其他具有竞争风险的疾病的个性化风险分层有希望.
- 虽然DL具有潜力,但DNFCR的临床相关性和优越性需要与传统的生存分析方法进行进一步验证.
- 该研究强调,在评估先进DL模型的好处时,需要仔细考虑数据特征和未测量的混因素.
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