Machine learning cohort demographics.

Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks to explore EHR predictors using existing patient data. Model 1 used patient diagnosis codes and was 75.5% accurate. Model 2 used patient prescriptions and was 64.9% accurate. Model 3 used both patient diagnosis codes and patient prescriptions and was 74.5% accurate. This suggests patient diagnosis codes are best able to predict opioid misuse. Opioid misusers have higher rates of drug abuse/mental health disorders than the general population, which could explain the performance of diagnosis predictors. In additional testing, Model 1 misclassified only 1.9% of negative cases (non-abusers), demonstrating a low type II error rate. This suggests further clinical implementation is viable. We hope to motivate future research to explore additional methods for universal opioid misuse screening.

电子健康档案（Electronic Health Records, EHR）能否在普通患者群体中预测阿片类药物滥用？本研究采用现有患者数据集，训练了三款反向传播神经网络，以探索可用于阿片类药物滥用预测的电子健康档案预测因子。其中，模型1仅纳入患者诊断编码，准确率达75.5%；模型2仅纳入患者处方信息，准确率为64.9%；模型3同时纳入患者诊断编码与处方信息，准确率为74.5%。结果表明，患者诊断编码是预测阿片类药物滥用的最优特征。

阿片类药物滥用者相较于普通人群，药物滥用与精神障碍的罹患率更高，这或可解释诊断编码预测模型的性能表现。在额外验证测试中，模型1仅错误分类了1.9%的阴性样本（非滥用者），展现出较低的II型错误率，提示该模型具备进一步临床转化应用的可行性。本研究期望能够推动后续相关研究，探索适用于全人群的阿片类药物滥用通用筛查方法。