School of Software, Shandong University, Jinan, 250101, China.
Department of Pediatric Surgery, Heze Municipal Hospital, Heze, 274000, China.
Interdiscip Sci. 2024 Jun;16(2):405-417. doi: 10.1007/s12539-024-00617-y. Epub 2024 Mar 15.
Survival analysis, as a widely used method for analyzing and predicting the timing of event occurrence, plays a crucial role in the medicine field. Medical professionals utilize survival models to gain insight into the effects of patient covariates on the disease, and the correlation with the effectiveness of different treatment strategies. This knowledge is essential for the development of treatment plans and the enhancement of treatment approaches. Conventional survival models, such as the Cox proportional hazards model, require a significant amount of feature engineering or prior knowledge to facilitate personalized modeling. To address these limitations, we propose a novel residual-based self-attention deep neural network for survival modeling, called ResDeepSurv, which combines the benefits of neural networks and the Cox proportional hazards regression model. The model proposed in our study simulates the distribution of survival time and the correlation between covariates and outcomes, but does not impose strict assumptions on the basic distribution of survival data. This approach effectively accounts for both linear and nonlinear risk functions in survival data analysis. The performance of our model in analyzing survival data with various risk functions is on par with or even superior to that of other existing survival analysis methods. Furthermore, we validate the superior performance of our model in comparison to currently existing methods by evaluating multiple publicly available clinical datasets. Through this study, we prove the effectiveness of our proposed model in survival analysis, providing a promising alternative to traditional approaches. The application of deep learning techniques and the ability to capture complex relationships between covariates and survival outcomes without relying on extensive feature engineering make our model a valuable tool for personalized medicine and decision-making in clinical practice.
生存分析是一种广泛应用于分析和预测事件发生时间的方法，在医学领域中起着至关重要的作用。医学专业人员利用生存模型深入了解患者协变量对疾病的影响，以及与不同治疗策略有效性的相关性。这些知识对于制定治疗计划和改进治疗方法至关重要。传统的生存模型，如 Cox 比例风险模型，需要大量的特征工程或先验知识来促进个性化建模。为了解决这些限制，我们提出了一种新的基于残差的自注意力深度神经网络生存模型，称为 ResDeepSurv，它结合了神经网络和 Cox 比例风险回归模型的优势。我们提出的模型模拟生存时间的分布以及协变量和结果之间的相关性，但不对生存数据的基本分布施加严格的假设。这种方法有效地考虑了生存数据分析中的线性和非线性风险函数。我们的模型在分析具有各种风险函数的生存数据方面的性能与其他现有的生存分析方法相当，甚至优于其他方法。此外，我们通过评估多个公开可用的临床数据集，验证了我们的模型相对于现有方法的优越性能。通过这项研究，我们证明了我们提出的模型在生存分析中的有效性，为传统方法提供了一种有前途的替代方案。深度学习技术的应用以及无需依赖广泛的特征工程来捕捉协变量和生存结果之间复杂关系的能力，使我们的模型成为个性化医疗和临床实践决策的有价值工具。
