近年来，数据驱动的远程医疗管理受到广泛关注，特别是在生存时间预测方面的应用。通过监测患者的身体特征指标，可以部署智能算法，实现高效的医疗管理。然而，这种纯粹的医疗数据驱动的场景通常缺乏多媒体信息，这给分析任务带来了挑战。针对这一问题，本文引入集成深度学习的思想来增强特征表示能力，从而增强远程医疗管理中的知识发现。因此，本文提出一种基于多视图深度学习的用于生存时间预测的高效医疗数据管理框架，简称“MDL-MDM”。首先对患者身体指标的基础监测数据进行编码，作为预测任务的数据基础。然后，选择卷积神经网络、图注意力网络和图卷积网络三种不同的神经网络模型构建混合计算框架。它们的结合可以带来多视图特征学习框架，以实现高效的医疗数据管理框架。此外，还对美国癌症患者的真实医学数据集进行了实验。结果表明，该提案可以预测生存时间，预测误差减少 1% 至 2%。

In recent years, data-driven remote medical management has received much attention, especially in application of survival time forecasting. By monitoring the physical characteristics indexes of patients, intelligent algorithms can be deployed to implement efficient healthcare management. However, such pure medical data-driven scenes generally lack multimedia information, which brings challenge to analysis tasks. To deal with this issue, this paper introduces the idea of ensemble deep learning to enhance feature representation ability, thus enhancing knowledge discovery in remote healthcare management. Therefore, a multiview deep learning-based efficient medical data management framework for survival time forecasting is proposed in this paper, which is named as "MDL-MDM" for short. Firstly, basic monitoring data for body indexes of patients is encoded, which serves as the data foundation for forecasting tasks. Then, three different neural network models, convolution neural network, graph attention network, and graph convolution network, are selected to build a hybrid computing framework. Their combination can bring a multiview feature learning framework to realize an efficient medical data management framework. In addition, experiments are conducted on a realistic medical dataset about cancer patients in the US. Results show that the proposal can predict survival time with 1% to 2% reduction in prediction error.