In the era of big data and artificial intelligence, healthcare data fusion analysis has become difficult because of the large amounts and different types of sources involved. Traditional methods are ineffective at processing and examination procedures for such complex multi-sensors of hyperscale healthcare data. To address this issue, we propose a novel large model-driven approach for hyperscale healthcare data fusion analysis in complex multi-sensor multi-sensors. Our method integrates data from various medical sensors and sources, using large models to extract and fuse information from structured and unstructured healthcare data. Then, we integrate these features with structured data using a hierarchical residual connected LSTM network, enhancing the model's ability to capture local and global context. Furthermore, we introduce a dynamic ReLU activation function and attention mechanism that allow us to adjust the depth of our networks dynamically while focusing only on relevant information. The experiments on MIMIC-III and eICU-CRD datasets demonstrate the superiority of the proposed method in terms of accuracy, efficiency, and robustness compared to state-of-the-art methods. Therefore, the proposed method provides valuable insights into the potential of large model-driven approaches for tackling the challenges of hyperscale healthcare data fusion analysis in complex multi-sensors.

中文翻译：

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在复杂的多传感器中进行大型模型驱动的超大规模医疗保健数据融合分析


在大数据和人工智能时代，由于涉及的数据量大且来源类型不同，医疗保健数据融合分析变得困难。传统方法在处理和检查如此复杂的超大规模医疗保健数据的多传感器程序时无效。为了解决这个问题，我们提出了一种新的大型模型驱动方法，用于在复杂的多传感器多传感器中进行超大规模医疗保健数据融合分析。我们的方法集成了来自各种医疗传感器和来源的数据，使用大型模型从结构化和非结构化医疗保健数据中提取和融合信息。然后，我们使用分层残差连接 LSTM 网络将这些特征与结构化数据集成，增强了模型捕获本地和全局上下文的能力。此外，我们引入了动态 ReLU 激活函数和注意力机制，使我们能够动态调整网络的深度，同时只关注相关信息。在 MIMIC-III 和 eICU-CRD 数据集上的实验表明，与最先进的方法相比，所提出的方法在准确性、效率和稳健性方面具有优越性。因此，所提出的方法为大型模型驱动方法在应对复杂多传感器中超大规模医疗保健数据融合分析挑战的潜力提供了有价值的见解。