Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction
Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction
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DOI：
10.1145/3534678.3539279
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Qianlong Wen;Z. Ouyang;Jianfei Zhang;Y. Qian;Yanfang Ye;Chuxu Zhang
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作者：
Qianlong Wen;Z. Ouyang;Jianfei Zhang;Y. Qian;Yanfang Ye;Chuxu Zhang
阿片类药物（例如，羟考酮和吗啡）是高度成瘾的处方（又称Rx）药物，其可以容易地被过度处方并导致阿片类药物过量。最近，阿片类药物的流行在美国越来越严重，因为其相关死亡人数以惊人的速度上升。为了打击致命的阿片类药物流行病，美国已经建立了一个国营处方药监测计划（PAPK），以缓解药物过度处方问题。尽管PAPs提供了与阿片类药物相关的详细处方史，但它仍然不足以防止阿片类药物过量，因为它无法预测过度处方的风险。此外，现有的基于机器学习的方法主要关注药物剂量，而忽略了患者历史记录背后的其他处方模式，从而导致性能不佳。为此，我们提出了一种新的模型DDHGNN -解纠缠动态异构图神经网络，用于过处方预测。具体来说，我们抽象的PADER数据到一个动态的异构图，全面描绘了处方和配药（P&D）的关系。然后，我们设计了一个动态异构图神经网络来学习患者的表征。此外，我们设计了一个对抗性的解缠器来学习一个解缠的表示，这是特别相关的处方模式。对1年匿名Pestival数据的广泛实验表明，DDHGNN优于最先进的方法，揭示了其在预防阿片类药物过量方面的前景。
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 prescription drug monitoring program (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. To this end, we propose a novel model DDHGNN - Disentangled Dynamic Heterogeneous Graph Neural Network, for over-prescribing prediction. Specifically, we abstract the PDMP data into a dynamic heterogeneous graph which comprehensively depicts the prescribing and dispensing (P&D) relationships. Then, we design a dynamic heterogeneous graph neural network to learn patients' representations. Furthermore, we 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.