ResDeepSurv: A Survival Model for Deep Neural Networks Based on Residual Blocks and Self-attention Mechanism.

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yuchen Wang, Xianchun Kong, Xiao Bi, Lizhen Cui, Hong Yu, Hao Wu
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Abstract

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.

Abstract Image

ResDeepSurv:基于残块和自我关注机制的深度神经网络生存模型
生存分析作为一种广泛应用于分析和预测事件发生时间的方法,在医学领域发挥着至关重要的作用。医学专家利用生存模型来深入了解患者协变量对疾病的影响,以及与不同治疗策略效果的相关性。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 比例危险模型,需要大量的特征工程或先验知识才能促进个性化建模。为了解决这些局限性,我们提出了一种用于生存建模的新型基于残差的自我关注深度神经网络,称为 ResDeepSurv,它结合了神经网络和 Cox 比例危险回归模型的优点。我们研究中提出的模型模拟了生存时间的分布以及协变量与结果之间的相关性,但并没有对生存数据的基本分布施加严格的假设。这种方法在生存数据分析中有效地考虑了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时,其性能与现有的其他生存分析方法相当,甚至更胜一筹。此外,我们还通过评估多个公开的临床数据集,验证了我们的模型与现有方法相比的卓越性能。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和捕捉协变量与生存结果之间复杂关系的能力,无需依赖大量的特征工程,使我们的模型成为临床实践中个性化医疗和决策的重要工具。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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