IEEE J Biomed Health Inform. 2020 Feb;24(2):424-436. doi: 10.1109/JBHI.2019.2929264. Epub 2019 Jul 17.
Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data. This paper develops a novel convolutional approach that addresses the drawbacks of both traditional statistical approaches as well as recent neural network models for survival. We present Match-Net: a missingness-aware temporal convolutional hitting-time network, designed to capture temporal dependencies and heterogeneous interactions in covariate trajectories and patterns of missingness. To the best of our knowledge, this is the first investigation of temporal convolutions in the context of dynamic prediction for personalized risk prognosis. Using real-world data from the Alzheimer's disease neuroimaging initiative, we demonstrate state-of-the-art performance without making any assumptions regarding underlying longitudinal or time-to-event processes-attesting to the model's potential utility in clinical decision support.
准确预测疾病轨迹对于早期识别和及时治疗高危患者至关重要。生存分析中的传统方法通常受到强参数假设的限制，并且从高维数据中学习的能力有限。本文提出了一种新颖的卷积方法，解决了传统统计方法以及最近的生存神经网络模型的缺点。我们提出了 Match-Net：一种具有缺失意识的时间卷积命中时间网络，旨在捕获协变量轨迹和缺失模式中的时间依赖关系和异质相互作用。据我们所知，这是首次在个性化风险预后的动态预测背景下对时间卷积进行的研究。使用来自阿尔茨海默病神经影像学倡议的真实世界数据，我们在不假设潜在的纵向或事件时间过程的情况下展示了最先进的性能-证明了该模型在临床决策支持中的潜在效用。
