"开发机器学习模型的自我报告和基于登记的数据,以预测青春期的饮食障碍"
Alexandros Katsiferis1, Andrea Joensen2, Liselotte Vogdrup Petersen3
1Section of Health Data Science & AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark. alexandros.katsiferis@sund.ku.dk.
Npj mental health research
|December 16, 2025
概括
预测青少年饮食障碍 (EDs) 是至关重要的. 研究人员使用出生队列数据开发了模型,识别了关键风险因素,如情绪症状和身体不满,用于早期检测和预防.
科学领域:
- 精神病学是一个精神病学.
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 在青春期早期发现和预防饮食障碍 (EDs) 是关键的公共卫生挑战.
- 青少年ED需要有效的预测工具,以便及时干预.
研究的目的:
- 开发和验证用于预测青少年ED的诊断和预后模型.
- 确定ED风险评估在早期和晚青春期的关键预测因素.
主要方法:
- 利用了来自丹麦国家出生队列的44357名参与者的数据.
- 训练有素的机器学习和后勤回归模型,使用自报告和注册表数据的~100个预测器.
- 在诊断和预测任务中使用接收器运行特征曲线 (AUC) 下的区域评估模型性能.
主要成果:
- 机器学习模型实现了诊断AUC的81.3%和预后AUC的76.9%.
- 使用前10个预测指标的物流回归显示了可比的性能.
- 发现的关键预测因素包括性别,情绪症状,同行/行为问题,压力,父母的BMI,身体不满以及早期的BMI.
结论:
- 开发的模型显示了在青少年ED中支持临床风险评估的潜力.
- 模型可以帮助识别个人进行低风险的预防性干预.
- 需要进一步的验证研究来评估现实世界的临床有效性.
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