This study proposes a new model for over-prescribing prediction.
In this study the authors propose a novel model DDHGNN - Disentangled Dynamic Heterogeneous Graph Neural Network, for over-prescribing prediction. Specifically, the authors abstract the prescription drug monitoring program (PDMP) data into a dynamic heterogeneous graph which comprehensively depicts the prescribing and dispensing (P&D) relationships. Then, the authors design a dynamic heterogeneous graph neural network to learn patients' representations. Furthermore, the authors devise an adversarial disentangler to learn a disentangled representation which is particularly related to the prescribing patterns. Extensive experiments on a 1-year anonymous PDMP data demonstrate that DDHGNN outperforms state-of-the-art methods, revealing its promising future in preventing opioid overdose. Opioids (e.g., oxycodone and morphine) are highly addictive prescription (aka Rx) drugs which can be easily overprescribed and lead to opioid overdose. Recently, the opioid epidemic is increasingly serious across the US as its related deaths have risen at alarming rates. To combat the deadly opioid epidemic, a state-run PDMP has been established to alleviate the drug over-prescribing problem in the US. Although PDMP provides a detailed prescription history related to opioids, it is still not enough to prevent opioid overdose because it cannot predict over-prescribing risk. In addition, existing machine learning-based methods mainly focus on drug doses while ignoring other prescribing patterns behind patients' historical records, thus resulting in suboptimal performance. (Published Abstract Provided)