预测个人对基于网络的积极心理学干预的反应:一种机器学习方法
Amanda C Collins1,2,3, George D Price1,4, Rosalind J Woodworth5
1Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
The journal of positive psychology
|June 10, 2024
概括
机器学习可以预测谁将从积极心理学干预 (PPI) 中受益. 这有助于将个人与最有效的幸福和抑郁症症状管理策略相匹配.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 数字健康数字健康
背景情况:
- 积极心理学干预 (PPI) 有效地增强幸福感并减少抑郁症状.
- 基于网络的PPI是常见的,但并非所有人都同样受益.
- 识别可能从基于网络的PPI中受益的个人对于个性化干预策略至关重要.
研究的目的:
- 利用机器学习来预测个人对基于网络的积极心理学干预措施的反应.
- 确定基线预后指标,预测个人从PPI中受益的可能性.
主要方法:
- 采用机器学习模型来分析120名参与者的基线数据.
- 评估了基线特征对幸福感和抑郁症状变化的预测准确度.
主要成果:
- 机器学习模型在预测结果方面显示了适度的相关性.
- 幸福感变化的预测准确度是r = 0.30 ± 0.09.
- 抑郁症状变化的预测准确度为r = 0.39 ± 0.06.
结论:
- 基线特征可以预测基于Web的PPI的治疗结果.
- 机器学习为预测个人对 PPI 的反应提供了一种可行的方法.
- 这些发现对调整干预措施以满足个体需求具有重大临床影响.
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