Clinical decision support systems are widely used to assist with medical decision making. However, clinical decision support systems typically require manually curated rules and other data that are difficult to maintain and keep up to date. Recent systems leverage advanced deep learning techniques and electronic health records to provide a more timely and precise result. Many of these techniques have been developed with a common focus on predicting upcoming medical events. However, although the prediction results from these approaches are promising, their value is limited by their lack of interpretability. To address this challenge, we introduce CarePre, an intelligent clinical decision assistance system. The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his or her historical medical records. The system includes an interactive framework together with intuitive visualizations designed to support diagnosis, treatment outcome analysis, and the interpretation of the analysis results. We demonstrate the effectiveness and usefulness of the CarePre system by reporting results from a quantities evaluation of the prediction algorithm, two case studies, and interviews with senior physicians and pulmonologists.