People with type 1 diabetes mellitus (T1DM) must continuously monitor their blood glucose levels and regulate them by insulin dosing to stay in a safe range. A reliable glucose prediction technique could pre-alert the risk of abnormal glycaemia in the near future. In this paper, we apply an attention-based deep network for glucose prediction, which models both the temporal dependencies and the physiological relations among glucose and glucose-related life events, including insulin and carbohydrate intake. We also propose to make use of the knowledge learned from non-target subjects with the same disease to improve the personalised prediction by applying parameters transfer. Our approach was evaluated on the standard benchmark OhioT1DM dataset, where the experiments achieved average root mean square errors over the 12 subjects of 17.82 mg/dL for 30 minutes and 28.54 mg/dL for 60 minutes. Additionally, our ablation experiments indicated that the use of transfer learning constantly improved the prediction. On this basis, we conclude that our approach achieves state-of-the-art performance with statistical significance, and data from other people with T1DM could help on improving personalised predictions. We release our codes at https://github.com/r-cui/GluPred under the MIT license.