Abstract: With the rapid development of big data analysis and deep learning, time series forecasting methods have been widely used in domains of medicine, finance, meteorology, transportation, and so on, which provide various types of decision supports for many application tasks. To solve the problem of low feature dimension ubiquitously in medical data and the problem of overfitting occurred by small samples in the existing deep learning frameworks, this paper focuses on the task of medical time series forecasting with small samples, and develops a new framework with trend embedding and granularity enhancement network. First, a granularity enhancement module is constructed with tricks of convolution operations, which is used to augment the dimensions of the samples of medical time series respectively in time-steps and data features for generating three-dimensional tensor. Second, the first-order difference of each sample is taken as the direction vector to learn the corresponding trend embedding by mapping with its direction derivative. Third, a static spatial adjacency matrix and a dynamic temporal adjacency matrix are simultaneously constructed to learn the spatiotem-poral embedding by a spatiotemporal Graph Convolutional Network (GCN). Finally, the constructed modules of spatial-temporal embedding, prediction embedding and trend embedding are integrated together into the architectural framework of the GCN, the gated recurrent unit and the residual network, which is trained to achieve the goal of medical time series forecasting. Extensive experimental results on four datasets including Cancer, ILI, Baries and COVID-19 show that the proposed approach in this paper reduces the three evaluation metrics (MAE, MAPE, RMSE) by 34.0607, 0.0107, 70.6728; 11.1808, 0.0950, 20.7285; 0.3546, 0.1127, 0.4553; 449.2437, 0.0144, 1 174.7273, compared with the best baseline model T-GCN. This illustrates the performance of the proposed method is superior than those of the baseline methods, indicating its feasibility and validation.