Department of Physical Education, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen, Fujian, 361024, People's Republic of China.
School of Physical Education, Guizhou Normal University, Guizhou, 550025, People's Republic of China.
BMC Psychiatry. 2024 Aug 27;24(1):581. doi: 10.1186/s12888-024-06017-2.
Precisely estimating the probability of mental health challenges among college students is pivotal for facilitating timely intervention and preventative measures. However, to date, no specific artificial intelligence (AI) models have been reported to effectively forecast severe mental distress. This study aimed to develop and validate an advanced AI tool for predicting the likelihood of severe mental distress in college students.
A total of 2088 college students from five universities were enrolled in this study. Participants were randomly divided into a training group (80%) and a validation group (20%). Various machine learning models, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were employed and trained in this study. Model performance was evaluated using 11 metrics, and the highest scoring model was selected. In addition, external validation was conducted on 751 participants from three universities. The AI tool was then deployed as a web-based AI application.
Among the models developed, the eXGBM model achieved the highest area under the curve (AUC) value of 0.932 (95% CI: 0.911-0.949), closely followed by RF with an AUC of 0.927 (95% CI: 0.905-0.943). The eXGBM model demonstrated superior performance in accuracy (0.850), precision (0.824), recall (0.890), specificity (0.810), F1 score (0.856), Brier score (0.103), log loss (0.326), and discrimination slope (0.598). The eXGBM model also received the highest score of 60 based on the evaluation scoring system, while RF achieved a score of 49. The scores of LR, DT, and SVM were only 19, 32, and 36, respectively. External validation yielded an impressive AUC value of 0.918.
The AI tool demonstrates promising predictive performance for identifying college students at risk of severe mental distress. It has the potential to guide intervention strategies and support early identification and preventive measures.
精确估计大学生心理健康挑战的概率对于促进及时干预和预防措施至关重要。然而，迄今为止，尚无特定的人工智能 (AI) 模型被报道能够有效地预测严重的精神困扰。本研究旨在开发和验证一种用于预测大学生严重精神困扰可能性的先进 AI 工具。
本研究共纳入来自五所大学的 2088 名大学生。参与者被随机分为训练组（80%）和验证组（20%）。本研究采用了各种机器学习模型，包括逻辑回归 (LR)、极端梯度提升机 (eXGBM)、决策树 (DT)、k-最近邻 (KNN)、随机森林 (RF) 和支持向量机 (SVM)，并对其进行了训练。使用 11 项指标评估模型性能，并选择得分最高的模型。此外，还对来自三所大学的 751 名参与者进行了外部验证。然后，将 AI 工具部署为基于网络的 AI 应用程序。
在所开发的模型中，eXGBM 模型的曲线下面积 (AUC) 值最高，为 0.932（95%CI：0.911-0.949），紧随其后的是 AUC 为 0.927 的 RF（95%CI：0.905-0.943）。eXGBM 模型在准确性（0.850）、精度（0.824）、召回率（0.890）、特异性（0.810）、F1 评分（0.856）、Brier 评分（0.103）、对数损失（0.326）和判别斜率（0.598）方面表现出卓越的性能。eXGBM 模型还根据评估评分系统获得了 60 分的最高分，而 RF 得分为 49 分。LR、DT 和 SVM 的得分分别为 19、32 和 36。外部验证产生了令人印象深刻的 AUC 值 0.918。
AI 工具在识别有严重精神困扰风险的大学生方面表现出有前景的预测性能。它有可能指导干预策略，并支持早期识别和预防措施。
