Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
Department of Biomedicine & Health Sciences, College of Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
PLoS One. 2021 Aug 2;16(8):e0255626. doi: 10.1371/journal.pone.0255626. eCollection 2021.
Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probability of these patients to continue outpatient treatment, predicting and managing patients who might discontinue treatment becomes necessary. Accordingly, we developed a machine learning (ML) algorithm to predict which the risk of patients dropping out of outpatient treatment schemes.
A total of 839 patients were selected out of 2,206 patients admitted for AUD in three hospitals under the Catholic Central Medical Center in Korea. We implemented six ML models-logistic regression, support vector machine, k-nearest neighbor, random forest, neural network, and AdaBoost-and compared the prediction performances thereof.
Among the six models, AdaBoost was selected as the final model for recommended use owing to its area under the receiver operating characteristic curve (AUROC) of 0.72. The four variables affecting the prediction based on feature importance were the length of hospitalization, age, residential area, and diabetes.
An ML algorithm was developed herein to predict the risk of patients with AUD in Korea discontinuing outpatient treatment. By testing and validating various machine learning models, we determined the best performing model, AdaBoost, as the final model for recommended use. Using this model, clinicians can manage patients with high risks of discontinuing treatment and establish patient-specific treatment strategies. Therefore, our model can potentially enable patients with AUD to successfully complete their treatments by identifying them before they can drop out.
酒精使用障碍（AUD）是一种慢性病，其复发率高于其他精神疾病。此外，患者需要持续接受门诊治疗以保持戒断。然而，这些患者继续接受门诊治疗的可能性较低，因此需要预测和管理可能停止治疗的患者。因此，我们开发了一种机器学习（ML）算法来预测患者退出门诊治疗方案的风险。
我们从韩国天主教中央医疗中心的三家医院的 2206 名 AUD 住院患者中选择了 839 名患者。我们实施了六种 ML 模型-逻辑回归、支持向量机、k-最近邻、随机森林、神经网络和 AdaBoost，并比较了它们的预测性能。
在六种模型中，AdaBoost 因其接收器操作特征曲线（AUROC）为 0.72 而被选为推荐使用的最终模型。基于特征重要性的四个影响预测的变量是住院时间、年龄、居住区域和糖尿病。
本文开发了一种用于预测韩国 AUD 患者停止门诊治疗风险的 ML 算法。通过测试和验证各种机器学习模型，我们确定了性能最佳的模型 AdaBoost 作为推荐使用的最终模型。使用该模型，临床医生可以对有高风险停止治疗的患者进行管理，并制定针对患者的治疗策略。因此，我们的模型可以通过在患者退出治疗之前识别他们，帮助 AUD 患者成功完成治疗。
