Abstract

Objective

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.

Methods

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.

Results

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).

Conclusions

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.

中文翻译：

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通过深度学习识别服用阿片类药物的患者阿片类药物使用障碍的风险

摘要

客观的

美国正在经历阿片类药物流行病。近年来，每年有超过 1000 万 12 岁或以上的阿片类药物滥用者。识别阿片类药物使用障碍 (OUD) 高风险的患者有助于进行早期临床干预以降低 OUD 的风险。我们的目标是开发和评估模型，使用电子健康记录和深度学习方法来预测阿片类药物患者的 OUD。由此产生的模型有助于我们更好地了解 OUD，为阿片类药物的流行提供新的见解。此外，这些模型为临床工具在 OUD 发生之前预测它提供了基础，从而允许早期干预。

方法

从 Cerner 的 Health Facts 数据库中提取了 2008 年 1 月 1 日至 2017 年 12 月 31 日期间服用含有活性阿片类药物成分药物的患者的电子健康记录。应用长短期记忆模型预测 OUD 风险目标遭遇之前的五次最近遭遇，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 分数、精度、召回率和接收者操作特征曲线下的面积来评估预测性能。

结果

长短期记忆 (LSTM) 模型提供了优于其他方法的有希望的预测结果，其 F1 得分为 0.8023（比密集神经网络 (DNN) 高约 0.016）和接收器操作特征曲线下面积 (AUROC) 0.9369（比 DNN 高约 0.145）。

结论

基于 LSTM 的顺序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，而先验领域知识最少。该工具有可能改善早期干预和预防阿片类药物流行病的临床决策支持。