OBF-Psychiatric是一组关于被诊断患有严重抑郁症,精神分裂症和ADHD的患者的运动活动数据集
Enrique Garcia-Ceja1, Andrea Stautland2, Michael A Riegler3
1Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, 64849, Mexico. enrique.gc@tec.mx.
Scientific data
|January 8, 2025
概括
本研究介绍了OBF-精神病学数据集,其中包括来自各种精神健康状况的个体的运动活动数据. 该资源有助于开发机器学习模型,以改善精神病学评估和患者护理.
科学领域:
- 数字健康数字健康
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 心理健康对整体福祉至关重要,预防策略显著影响疾病负担.
- 身体佩戴的传感器能够持续收集数据,以获得心理健康见解,从而有可能优化精神病学评估.
- 用于精神病学研究的高质量的医疗数据有限,特别是对于被诊断出来的患者.
研究的目的:
- 为了推进心理健康研究,引入OBF-精神病学数据集.
- 为机器学习模型开发提供全面的运动活动数据集.
- 通过数据驱动的洞察力,促进改善精神病学评估和患者结果.
主要方法:
- 收集了162个人的运动活动记录.
- 包括被诊断患有双相/单相重度抑郁症,精神分裂症和注意力缺陷多动症障碍 (ADHD) 的患者.
- 结合了来自情绪/焦虑障碍和健康对照组的临床样本的数据.
主要成果:
- OBF-精神病学数据集包括1565天的运动活动数据.
- 包含来自不同精神病患者群体和健康对照组的录音.
- 该数据集适用于构建和验证用于心理健康分析的机器学习模型.
结论:
- OBF-精神病学数据集是心理健康研究的宝贵资源.
- 这一数据集可以支持开发用于精神病评估的新型分析工具.
- 利用这些数据集可以带来更好的患者体验和更好的生活质量.
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