Abstract: Diagnosis prediction is an important prediction task in healthcare,which aims to predict the future diagnosis of patients based on their historical health records.Predictive models based on attention mechanisms and recurrent neural network are widely used to solve this task,but they are easy to be affected by insufficient data.In addition,medical domain knowledge plays an important role in improving the performance of diagnosis prediction,but existing methods still cannot make full use of those know-ledge.Therefore,a diagnostic prediction model based on graph convolutional network and attention mechanism is designed.Firstly,the medical ontology is used to model the correlation between medical concepts,then the patient visit information is modeled as a graph.Secondly,the graph convolution module is used to obtain the spatial features between the medical codes in each visit of the patient.Finally,a multi-head attention mechanism is used to model the interrelationship between visit features and multi-level medical knowledge to predict the future health status of patients.Experimental results on two publicly available medical datasets show that the diagnosis prediction performance of the model is better than that of the existing diagnostic prediction models,and the potential information in the medical knowledge graph can beused more effectively.

Key words: Diagnostic prediction, Electronic health record, Medical domain knowledge, Graph convolutional network, Attention mechanism