Abstract
rescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce Overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. Despite a few machine-learning-based methods that have been proposed for detecting overprescribing, they usually ignore the patient prescribing behavior and their performances are not satisfying. In light of this, we propose a novel model RxNet for overprescribing detection in PDMP. RxNet builds a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various Rx entries (e.g., patients) whose representations are encoded by graph neural network. In addition, to explore the dynamic Rx-refill behavior and medical condition variation of patients, an RxLSTM network is designed to update representations of patients. Based on the output of RxLSTM, a dosing-adaptive network is leveraged to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a 1-year Ohio PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse, with an average of 5.7% and 7.3% improvement on F1 score respectively