Intelligent Hospital Guidance System based on MultiRound Conversation Registering a wrong hospital department is common when patients use online registering systems. Currently, there are some systems in practice. However, patients are unable to choose the best department due to different names and authorities of hospitals. To help solve the problem, we build a symptomdiseasedisciplinary knowledge graph to recommend appropriate departments for patients. We obtain real diseasedisciplinary information based on the regional health platform electronic health records (EHRs). Besides, we synthesize the symptomdisease relationship between ICD codes and medical encyclopedia websites. To further help the system predict the diseases based on patientsu0027 complaints, we update the weights of diseases through patientsu0027 choices in multiround conversations. Experimental results show that the accuracy of final prediction is up to 92%.

KnowRisk: An Interpretable KnowledgeGuided Model for Disease Risk Prediction Thanks to the widespread adoption of Electronic Health Record (EHR) systems, a variety of datadriven clinical risk prediction approaches have been spawned in recent years. However, there remain three challenges, which if addressed would improve the performance and applicability of such models. (i) Due to the limited data sharing between different health care institutions, the EHR data collected by a single institution is often inadequate or missing some visits records. The limited number of data cannot meet the large sample required of recent approaches especially deep learning models. In addition, the missing records (due to visiting different institution) may contain important health condition of the patient, which if ignored would cause prediction bias. (ii) Few existing approaches take clinical knowledge into account. The auxiliary knowledge if included can greatly reduce the data dependency of many modern learning algorithms. (iii) Most existing deep learning based methods are unable to identify the contribution of each medical event to the final results, which prohibits such models from being widely accepted in practical clinical applications. In this paper, we propose an interpretable and knowledgeguided deep model to address these challenges. Specifically, we distill knowledge from a clinical knowledge graph both explicitly and implicitly, which can not only supplement inadequate patient records but also guide the predicting process of the model. Furthermore, skipconnections and attention mechanisms are adopted to improve the interpretability of our model. In the context of heart failure prediction task, our model outperforms several stateoftheart methods. Finally, a series of case studies are presented to prove the interpretability of our model.