使用严的游戏数据来预测注意力缺陷/多动障碍儿童的WISC-V处理速度指数的新方法:机器学习研究
Jun-Su Kim1, Yoo Joo Jeong1,2,3, Seung-Jae Kim1
1AI-based Neurodevelopmental Diseases Digital Therapeutics Group, Korea Brain Research Institute (KBRI), 61, Cheomdan-ro, Daegu, 41062, Republic of Korea.
JMIR serious games
|October 14, 2025
概括
机器学习模型可以使用严的游戏数据预测ADHD儿童的处理速度指数 (PSI) 成绩. 这为监测认知功能和支持治疗规划提供了一种新的,易于使用的方法.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 发展心理学 发展心理学
背景情况:
- 处理速度指数 (PSI) 来自韩国儿童维克斯勒智力尺度-第五版 (K-WISC-V) 是患有多动症儿童认知功能的关键指标.
- 目前测试频率的限制阻碍了对PSI的短期监测,这对于及时干预ADHD至关重要.
- 需要可访问,客观的方法来预测PSI得分,以更好地管理ADHD.
研究的目的:
- 开发一种机器学习模型,用于预测ADHD儿童的PSI分数.
- 利用来自严游戏的行为数据进行客观和可访问的认知功能监测.
主要方法:
- 招募了68名被诊断患有多动症的儿童 (6-13岁);数据排除后包括了59名参与者.
- 进行K-WISC-V初始PSI评估,随后进行25分钟的严游戏内容参与.
- 使用游戏会话数据训练机器学习模型,并使用RMSE,MAE和MAPE评估预测性能,采用4倍交叉验证.
主要成果:
- 支持向量回归 (SVR) 单独表现强.
- 集成AdaBoost,Elastic Net和SVR的整体模型实现了最好的预测准确性,其中RMSE (10.072),MAE (6.798) 和MAPE (6.611%) 是最低的.
- 整体模型在PSI得分的平均值 (100) 周围显示了最高的准确性,这表明了临床可靠性.
结论:
- 开发的机器学习模型提供了一个潜在的客观和可访问的工具,用于监测ADHD儿童的认知功能.
- 这种方法可以补充传统的评估,允许持续跟踪症状变化和个性化治疗计划.
- 建议在不同人群中进行进一步的验证,并在临床环境中检查长期可行性.
关键词:
更多关于 ADHD ADHD 的文章严的游戏是严的游戏.注意力缺陷多动性障碍注意力缺陷多动性障碍数字治疗学数字治疗学机器学习是机器学习.预测 预测 预测 预测处理速度 处理速度.症状跟踪 症状跟踪 症状跟踪更多相关视频
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