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In-hospital clinical deterioration is a major worldwide healthcare burden in the intensive care units (ICUs), as it requires rapid intervention. Rapid response systems (RRSs) are widely used in many hospitals for the early detection of clinical deterioration to prevent cardiac arrest. Recently, with the increasing use of deep learning (DL) and electronic health records (EHR), many DL models have been developed for the intensive care domain, such as prediction of cardiac arrest, sepsis, or transferring to ICU. However, most existing methods do not explicitly learn the structure of multivariate time-series data, and this leads to high false-alarm rates and low sensitivity. In this research, we propose a novel DL-based framework that interpolates high-dimensional sequential data. Our approach combines two graph neural networks with an attention mechanism to learn the complex dependencies among multivariate time series. The experiments were conducted on two datasets: a private clinical dataset collected from Chonnam National University Hospital (CNUH) and a public dataset from the University of Virginia (UV). The experimental results show the potential performance of our model compared to some other related research.