The broad adoption of electronic health records (EHR) data and the availability of biomedical knowledge graph (KG) on the web have provided clinicians and researchers unprecedented resources and opportunities for conducting health risk predictions to improve healthcare quality and medical resource allocation. Existing methods have focused on improving the EHR feature representations using attention mechanisms, time-aware models, or external knowledge. However, they ignore the importance of using personalized information to make predictions. Besides, the reliability of their prediction interpretations needs to be improved since their interpretable attention scores are not explicitly reasoned from disease progression paths. In this paper, we propose MedPath to solve these challenges and augment existing risk prediction models with the ability to use personalized information and provide reliable interpretations inferring from disease progression paths. Firstly, MedPath extracts personalized knowledge graphs (PKGs) containing all possible disease progression paths from observed symptoms to target diseases from a large-scale online medical knowledge graph. Next, to augment existing EHR encoders for achieving better predictions, MedPath learns a PKG embedding by conducting multi-hop message passing from symptoms node to target disease nodes through a graph neural network encoder. Since MedPath reasons disease progression by paths existing in PKGs, it can provide explicit explanations for the prediction by pointing out how observed symptoms can finally lead to target diseases. Experimental results on three real-world medical datasets show that MedPath is effective in improving the performance of eight state-of-the-art methods with higher F1 scores and AUCs. Our case study also demonstrates that MedPath can greatly improve the explicitness of the risk prediction interpretation.