Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.
Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA.
J Am Med Inform Assoc. 2021 Jul 30;28(8):1683-1693. doi: 10.1093/jamia/ocab043.
The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions.
Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner's Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve.
The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN).
LSTM-based sequential deep learning models can accurately predict OUD using a patient's history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.
美国正经历一场阿片类药物泛滥危机。近年来，每年有超过 1000 万 12 岁及以上的阿片类药物滥用者。识别出患有阿片类药物使用障碍（OUD）高风险的患者有助于尽早进行临床干预，降低 OUD 风险。我们的目标是使用电子健康记录和深度学习方法开发和评估预测接受阿片类药物治疗的患者发生 OUD 的模型。这些模型有助于我们更好地了解 OUD，为阿片类药物泛滥提供新的见解。此外，这些模型为预测 OUD 提供了基础，以便在其发生之前进行早期干预。
从 Cerner 的 Health Facts 数据库中提取了 2008 年 1 月 1 日至 2017 年 12 月 31 日期间有过阿片类药物处方的患者的电子健康记录。应用长短时记忆模型（LSTM）基于目标就诊前最近的五次就诊预测 OUD 风险，并与逻辑回归、随机森林、决策树和密集神经网络进行比较。使用 F1 评分、精度、召回率和接收者操作特征曲线下的面积（AUROC）评估预测性能。
LSTM 模型提供了有前景的预测结果，优于其他方法，F1 得分为 0.8023（比密集神经网络（DNN）高约 0.016），AUROC 为 0.9369（比 DNN 高约 0.145）。
基于 LSTM 的序列深度学习模型可以使用患者的电子健康记录历史准确预测 OUD，所需的先验领域知识很少。该工具有可能改善临床决策支持，以便进行早期干预和预防，以应对阿片类药物泛滥危机。
