Department of Psychiatry, Gil Medical Center, Incheon, Korea.
Department of Energy and Information Technology, Gachon University, Seongnam-si, Republic of Korea.
Psychiatry Res. 2020 Nov;293:113474. doi: 10.1016/j.psychres.2020.113474. Epub 2020 Sep 24.
Depression is a mental illness that causes significant disturbances in daily life. Depression is commonly associated with low mood, severe health problems, and substantial socioeconomic burden; hence, it is necessary to be able to detect depression earlier. We utilized the medical check-up cohort database of the National Health Insurance Sharing Service in Korea. We split the total dataset into training (70%) and test (30%) sets. Subsequently, five-fold cross validation was performed in the training set. The holdout test set was only used in the last step to evaluate the performance of the predictive model. Random forest algorithm was used for the predictive model. The analysis included 433,190 individuals who had a national medical check-up from 2009-2015, which included 10,824 (2.56%) patients in the depression group. The area under the receiver-operating curve was 0.849. Other performance metrics included a sensitivity of 0.737, specificity of 0.824, positive predictive value of 0.097, negative predictive value of 0.992, and accuracy of 0.780. Our predictive model could contribute to proactively reducing depression prevalence by administering interventions to prevent depression in patients receiving medical check-up. Future studies are needed to prospectively validate the predictability of this model.
抑郁症是一种精神疾病，会导致日常生活中的严重紊乱。抑郁症通常与情绪低落、严重的健康问题和巨大的社会经济负担有关；因此，有必要更早地发现抑郁症。我们利用了韩国国家健康保险共享服务的体检队列数据库。我们将总数据集分为训练集（70%）和测试集（30%）。随后，在训练集中进行了五折交叉验证。保留测试集仅用于最后一步，以评估预测模型的性能。我们使用随机森林算法来构建预测模型。分析包括了 2009 年至 2015 年间接受国家体检的 433,190 人，其中有 10,824（2.56%）名患者患有抑郁症。接收者操作特征曲线下的面积为 0.849。其他性能指标包括敏感性为 0.737、特异性为 0.824、阳性预测值为 0.097、阴性预测值为 0.992和准确性为 0.780。我们的预测模型可以通过对接受体检的患者进行干预来预防抑郁症，从而有助于主动降低抑郁症的发病率。需要进一步的前瞻性研究来验证该模型的可预测性。
