QuBBD: Deep Poisson Methods for Biomedical Time-to-Event and Longitude Data

拟议的研究通过开发适当的工具，从可用的大数据中获得新的见解，并通过将复杂的机器学习方法应用于生物医学研究人员熟悉的框架，直接解决了NIH BD2K计划的使命。这种新方法将是第一个能够将机器学习技术与事件时间和连续纵向结果数据结合起来的方法之一，也是深度泊松模型的第一个扩展。从本质上讲，这项工作在生物医学和临床研究人员对先进预后和预测技术的需求与深度学习方法在纵向收集生物医学数据的背景下未实现的潜力之间建立了缺失的桥梁。为了促进临床研究的顺利采用，结果将通过出版物和描述良好的软件包翻译成应用从业者熟悉的术语。所开发的方法的应用将使用来自NIH dbGAP存储库的数据来说明，从而进一步促进开放获取数据源的使用。

The proposed research directly addresses the mission of NIH's BD2K initiative by developing appropriate tools to derive novel insights from available Big Data and by adapting sophisticated machine learning methodology to a framework familiar to biomedical researchers. This new methodology will be one of the first to enable use of machine learning techniques with time-to-event and continuous longitudinal outcome data, and will be the first such extension of the deep Poisson model. In essence, this undertaking builds the missing bridge between the need for advanced prognostic and predictive techniques among biomedical and clinical researchers and the unrealized potential of deep learning methods in the context of biomedical data collected longitudinally. To facilitate smooth adoption in clinical research, the results will be translated into terms familiar to applied practitioners through publications and well-described software packages. The application of the methodology developed will be illustrated using data from the NIH dbGAP repository, thereby further promoting the use of open access data sources.