GATE: Graph-Attention Augmented Temporal Neural Network for Medication Recommendation

Abstract

Medication recommendation based on Electronic Health Records (EHRs) is an important research direction, which aims to make prescription recommendations according to EHRs of patients. Most existing methods either only make recommendation through EHRs of the current admission while ignoring the patient's historical records, or fail to fully consider the correlations among the clinical events from every single admission. These methods have shown their limitations in dealing with the complex structural correlations and temporal dependencies of clinical events in EHRs, which results in the defect of recommendation quality as well as the lack of temporal prediction ability. For that, a novel graph-attention augmented temporal neural network is proposed to model both the structural and temporal information simultaneously. For each admission record, a co-occurrence graph is constructed to establish the correlations among clinical events, and then a graph-attention augmented mechanism is used to learn the structural correlations on the graphs to obtain better representation of this admission. Then a temporal updating module based on the gated recurrent units is further proposed to learn the temporal dependencies between multiple admissions of each patient. Furthermore, our proposed model is also constrained by the co-occurrence graph, which can capture the internal correlations of clinical events and provide better modeling capability when the training data is sparse. Experiments illustrate that our model is superior to the state-of-the-art methods on a real-world dataset MIMIC-III in all effectiveness measures.