Survival analysisDeep Learning
DeepHit
DeepHit 是一个用于竞争风险生存分析的深度神经网络框架。由 Lee 等人于 2018 年提出，它扩展了 DeepSurv 的能力，以处理可能发生多种互斥事件的情况，例如特定疾病死亡与死于其他原因。DeepHit 解决了当受试者可能经历不同类型的终末事件时进行个性化风险预测的挑战，这在医学和可靠性应用中是很常见的情景。
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来源
- Lee, C., Zame, W., Yoon, J., & van der Schaar, M. (2018). DeepHit: A deep learning approach for dynamic survival analysis with competing risks. AAAI Conference on Artificial Intelligence, 32(1), 2314–2321. link ↗
- Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. DOI: 10.1080/01621459.1999.10474144 ↗
- Katzman, J. L., et al. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. Journal of Machine Learning Research, 40, 40–51. DOI: 10.1186/s12874-018-0482-1 ↗
如何引用本页
ScholarGate. (2026, June 3). Deep Learning for Competing Risks. ScholarGate. https://scholargate.app/zh/survival/deephit
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