PDF(Pubmed)
Abstract：
Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations.
Our data originated from the National Health and Nutrition Examination Survey (2005-2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score.
Deep learning had the highest AUC (0.891, 95%CI 0.869-0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904-0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors.
Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.
Our data originated from the National Health and Nutrition Examination Survey (2005-2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score.
Deep learning had the highest AUC (0.891, 95%CI 0.869-0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904-0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors.
Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.
摘要：
背景：抑郁症是退伍军人中常见的心理健康问题，死亡率高。尽管进行了大量的调查，对抑郁症危险因素的预测和识别仍然受到严重限制。这项研究使用深度学习算法来识别退伍军人的抑郁症及其与临床表现相关的因素。
方法：我们的数据来自国家健康和营养检查调查（2005-2018）。使用深度学习和五种具有10倍交叉验证的传统机器学习算法确定了2,546名退伍军人的数据集。通过检查受试者工作特性曲线(AUC)下的面积来评估模型性能,准确度,召回，特异性，精度,F1得分。
结果：深度学习在识别退伍军人抑郁症方面具有最高的AUC（0.891，95CI0.869-0.914）和特异性（0.906）。对不同年龄段退伍军人抑郁症的进一步研究显示，中年组深度学习的AUC值为0.929（95CI0.904-0.955），老年组的AUC值为0.924（95CI0.900-0.948）。除了一般的健康状况，睡眠困难,记忆障碍,丧失工作能力,收入,BMI,和慢性病，维生素E和C等因素，棕榈酸也被确定为重要的影响因素。
结论：与传统机器学习方法相比，深度学习算法实现了最佳性能，有助于识别退伍军人中的抑郁症及其危险因素。
方法：我们的数据来自国家健康和营养检查调查（2005-2018）。使用深度学习和五种具有10倍交叉验证的传统机器学习算法确定了2,546名退伍军人的数据集。通过检查受试者工作特性曲线(AUC)下的面积来评估模型性能,准确度,召回，特异性，精度,F1得分。
结果：深度学习在识别退伍军人抑郁症方面具有最高的AUC（0.891，95CI0.869-0.914）和特异性（0.906）。对不同年龄段退伍军人抑郁症的进一步研究显示，中年组深度学习的AUC值为0.929（95CI0.904-0.955），老年组的AUC值为0.924（95CI0.900-0.948）。除了一般的健康状况，睡眠困难,记忆障碍,丧失工作能力,收入,BMI,和慢性病，维生素E和C等因素，棕榈酸也被确定为重要的影响因素。
结论：与传统机器学习方法相比，深度学习算法实现了最佳性能，有助于识别退伍军人中的抑郁症及其危险因素。
