Next admission diagnosis event prediction is one of the core tasks based on electronic health records. Existing researches on this task mainly focus on learning accurate disease representations and then fusing these disease representations into deep learning models for learning and prediction. This approach implicitly models the disease progressing path between diseases in the model, while ignoring the explicit disease progression path between diseases that can be established by the data itself. Therefore, it is impossible to accurately and explicitly represent the disease progression path to achieve the purpose of interpreting the disease progression path. For this problem, this paper propose the Hybrid Rule-Transformer Network, it first represent the interpretable disease progression paths as positive region-based decision rules by Pawlak Rough Set, then fuse the original disease embedding and the rule embedding by a dynamic gating fusion strategy, and finally realize the extraction of patient time series modeling information through the Transformer layer. Extensive experiments on two real EHR datasets show that the model this paper established has achieved state-of-the-art in terms of F1-score and Recall, and provides process interpretability of the disease progression paths between visits.