情绪预测:一种基于变压器的方法
Leire Paz-Arbaizar1, Jorge Lopez-Castroman2,3,4, Antonio Artés-Rodríguez1,3,5,6
1Signal Theory and Communications Department, Universidad Carlos III de Madrid, Leganés, Spain.
Journal of medical Internet research
|March 18, 2025
概括
这项研究引入了一种用于实时心理健康监测的新型深度学习方法,使用移动设备数据预测精神病患者的情绪状态和潜在危机.
科学领域:
- 数字精神病学数字精神病学
- 计算心理健康 计算心理健康
- 机器学习在医疗保健中的应用
背景情况:
- 由于主观评估和环境影响,精神病患者监测具有挑战性.
- 实时监测对于管理精神疾病中精神状态的变化至关重要.
研究的目的:
- 通过深度学习和移动设备数据开发客观的实时患者监测.
- 预测患者的自我报告并检测突然的情绪价值变化,以便及时进行临床干预.
主要方法:
- 利用基于证据的行为 (eB2) 应用程序进行被动和自我报告的数据收集.
- 应用隐藏的马尔科夫模型 (HMM) 缺失的数据和变压器深度神经网络的时间序列预测.
- 采用分类算法来预测情绪状态和患者健康问卷-9 (PHQ-9) 的答案.
主要成果:
- 获得了高精度 (0.93) 和ROC AUC (0.98) 的情感价值分类.
- 成功预测的情绪状态变化提前一天发生 (ROC AUC 0.87).
- 对于PHQ-9反应,包括自杀念头 (Q9:准确率0.9,ROC AUC 0.77) 的强有力的预测性表现.
结论:
- 将HMM预处理与变压器模型相结合,用于稳定的多变量时间序列预测,性能优于传统方法.
- 展示了被动变量在预测患者情绪状态和问卷分数方面的潜力.
- 实现实时监控,以改善精神病治疗中的风险检测和治疗调整.
关键词:
在 PHQ-9 中.患者健康调查问卷-9影响影响影响影响影响影响.情绪的价值 情绪的价值机器学习是机器学习.精神障碍 精神障碍是一种精神障碍.监控 监控 监控 监控 监控 监控情绪 情绪 情绪 情绪被动数据是被动数据.心理上的痛苦 心理上的痛苦时间序列预测时间序列预测更多相关视频
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