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

这项拟议的研究直接满足了NIH BD2K倡议的使命，开发了适当的工具，从现有的大数据中获得新的见解，并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。这种新的方法将是第一批能够使用机器学习技术的方法之一，这些技术具有事件间隔时间和连续的纵向结果数据，并且将是深度泊松模型的第一个这样的扩展。本质上，这项工作在生物医学和临床研究人员对高级预测和预测技术的需求与在纵向收集的生物医学数据背景下尚未实现的深度学习方法的潜力之间架起了一座缺失的桥梁。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被翻译成应用从业者熟悉的术语。将使用国家卫生研究院数据库数据库中的数据说明所开发方法的应用，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.