RxNet: Rx-refill Graph Neural Network for Overprescribing Detection
RxNet: Rx-refill Graph Neural Network for Overprescribing Detection
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DOI：
10.1145/3459637.3482465
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发表时间：
2021-10
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影响因子：
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通讯作者：
Jianfei Zhang;Ai-Te Kuo;Jianan Zhao;Qianlong Wen;E. Winstanley;Chuxu Zhang;Yanfang Ye
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作者：
Jianfei Zhang;Ai-Te Kuo;Jianan Zhao;Qianlong Wen;E. Winstanley;Chuxu Zhang;Yanfang Ye
处方药（又名Rx）很容易被过量使用，导致药物滥用或阿片类药物过量。因此，美国已经制定了一项国家处方药监测计划（PDMP）来减少过度处方。然而，PDMP在检测患者潜在的过量处方行为方面能力有限，影响了其预防患者药物滥用和过量的有效性。尽管已经提出了一些基于机器学习的方法来检测过量处方，但它们通常忽略了患者的处方行为，并且它们的表现并不令人满意。鉴于此，我们提出了一种新的模型RxNet用于PDMP的过量处方检测。RxNet构建了一个动态异构图来建模Rx补药，Rx补药本质上是各种Rx条目（例如，患者）之间的处方和配药（P&D）关系，其表示由图神经网络编码。此外，为了探索患者的动态rx - fill行为和医疗状况变化，设计了一个RxLSTM网络来更新患者的表征。基于RxLSTM的输出，利用剂量自适应网络提取和重新校准剂量模式，获得精细的患者表征，最终用于过量处方检测。一项为期一年的俄亥俄州PDMP数据的广泛实验结果表明，RxNet在预测阿片类药物过量和药物滥用高风险患者方面始终优于最先进的方法，F1评分平均分别提高5.7%和7.3%。
Prescription (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.