从生物物理数据对抑郁症进行个性化情绪预测和解释
Sobhan Chatterjee1, Jyoti Mishra2, Frederick Sundram3
1Department of Electrical, Computer and Software Engineering, Faculty of Engineering, University of Auckland, Auckland 1010, New Zealand.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
这项研究引入了针对抑郁症的个性化深度学习模型,使用可穿戴数据准确预测情绪波动. 可解释的人工智能方法识别了恶化症状的关键因素,有助于量身定制的心理健康治疗.
科学领域:
- 数字健康数字健康
- 人工智能在心理健康中的作用
- 计算精神病学是一种计算精神病学.
背景情况:
- 全球心理健康需求超过了资源的可用性,突出了创新的数字健康解决方案的需要.
- 目前用于抑郁症的机器学习 (ML) 模型缺乏可解释性,尽管抑郁症具有多模式性质,但这阻碍了个性化治疗.
- 个性化和可解释的模型对于临床医生来说至关重要,以了解个人情绪下降触发因素和量身定制治疗.
研究的目的:
- 开发基于深度学习 (DL) 的个性化和准确的抑郁症情绪预测模型.
- 引入新的方法来识别加剧抑郁症状的关键因素.
- 通过可解释的AI见解来增强临床决策.
主要方法:
- 利用了一个多式数据集,包括生态瞬间评估,可穿戴的生活方式数据和14名患有轻度至中度抑郁症的参与者的神经认知评估.
- 开发了使用进化算法优化的分类和回归DL模型,以获得最大的预测性能.
- 采用可解释的人工智能技术 (SHAP,ALE,Anchors) 来解释模型预测和识别影响情绪的特征.
主要成果:
- 获得了情绪评分的高预测准确性,一些参与者的模型误差低至6%,使用五倍交叉验证对10个经典的ML模型进行验证.
- 通过可解释的人工智能方法成功识别了导致情绪状态下降的关键特征.
- 展示了DL模型为个性化抑郁管理提供可操作的见解的潜力.
结论:
- 个性化的DL模型在抑郁症中提供了准确的情绪预测,解决了当前黑子ML方法的局限性.
- 应用于这些模型的可解释的AI技术为个体症状恶化提供了关键的见解.
- 这种方法可以为临床医生提供数据驱动,个性化的抑郁症治疗干预措施.
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