从眼睛干燥到抑郁:基于机器学习的框架来预测青少年心理健康
Le Han1, Ying Liu2, Peng Xian3
1Beijing Tongren Hospital, Capital Medical University Hospital Infection Management & Disease Control and Prevention Department, Beijing, China.
BMC medical informatics and decision making
|November 6, 2025
概括
将干眼病 (DED) 添加到风险评估中,可以显著改善青少年抑郁症的预测. 这种方法可能有助于早期学校的识别和预防策略.
科学领域:
- 眼科医生 眼科 眼科
- 精神病学是一个精神病学.
- 公共卫生 公共卫生
背景情况:
- 青少年抑郁症是一个重要的公共卫生问题.
- 目前的抑郁风险评估工具往往忽略了身体健康指标.
- 干眼病 (DED) 在预测青少年抑郁症状方面的作用尚未得到充分研究.
研究的目的:
- 调查是否将干眼病 (DED) 纳入现有的风险评估模型可以提高青少年抑郁症状的预测.
- 评估机器学习分类器在预测青少年抑郁症时的有效性,当眼睛健康与心理社会因素一起考虑时.
主要方法:
- 分析了2076份青少年问卷,包括眼睛健康,睡眠模式,电子设备使用,社会支持和人口统计数据.
- 使用了通过交叉验证训练的五个机器学习分类器.
- 基于歧视 (AUC) 和校准的评估模型性能.
主要成果:
- 结合DED的模型显示出强大的预测歧视,AUC约为0.84.
- 这些模型显示出良好的校准,在识别没有抑郁和严重抑郁方面表现最好.
- 在轻度至中度抑郁症类别中,预测准确度较低.
结论:
- 将DED等眼睛健康指标与心理社会因素结合起来,可以提高机器学习对青少年抑郁症的预测.
- 这种方法有可能用于更早的,基于学校的风险青少年的识别和转诊.
- 这种低成本,基于问卷的方法显示出对人口查和有针对性的预防有希望,等待进一步验证.
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