情绪感知:智能手机应用程序用于数字表型和评估双相情感障碍
Jia-Hao Hsu1, Chung-Hsien Wu1, Esther Ching-Lan Lin2
1Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan.
Psychiatry research
|February 24, 2024
概括
该MoodSensing应用程序有效地跟踪双相情感障碍评估的数字表型,在预测症状得分方面显示出高准确性. 这款经过验证的应用程序确保了用户的隐私和参与,为临床评估提供了可靠的工具.
科学领域:
- 数字化表型化是指数字化表型化.
- 双极性障碍研究研究
- 移动健康应用 移动健康应用
背景情况:
- 日常生活跟踪有助于评估双相情感障碍.
- 现有的应用程序往往缺乏学术验证,隐私政策和长期支持.
- 在心理健康领域需要经过验证,专注于隐私的数字工具.
研究的目的:
- 开发和评估MoodSensing应用程序用于双相情感障碍评估.
- 收集用户数字表型化数据,以获得临床见解.
- 应用深度学习来预测症状严重程度.
主要方法:
- 开发了MoodSensing应用程序,并为用户体验和临床需求提供临床医生的意见.
- 收集的数字表型化数据经机构审查委员会批准.
- 利用深度学习来预测每周的汉密尔顿抑郁症评分表 (HAM-D) 和年轻狂热评分表 (YMRS) 的分数.
主要成果:
- 该应用程序准确地预测了HAM-D和YMRS得分,平均绝对误差分别为0.84和0.22.
- 收集的数据证明了临床评估的可区分性.
- 观察到用户参与度的统计学上显著增加.
结论:
- 该MoodSensing应用程序提供了一个积极的用户体验和有价值的临床评估数据.
- 该应用程序包含了强大的隐私政策.
- 在果商店和谷歌游戏商店上提供MoodSensing,为双相情感障碍管理提供了经过验证的工具.
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