使用机器学习来预测在COVID-19大流行期间对压力进行在线自我引导干预的吸收率
Gavin N Rackoff1, Michelle G Newman1
1Department of Psychology, The Pennsylvania State University, University Park, Pennsylvania, USA.
机器学习模型可以预测在线心理健康干预措施的采用. 对治疗和性少数群体地位的兴趣增加了吸收率,而男性性别减少了,为未来的干预策略提供了信息.
科学领域:
- 数字心理健康数字心理健康
- 机器学习在医疗保健中的应用.
- 公共卫生干预措施 公共卫生干预措施
背景情况:
- 在线自我引导的干预措施对心理健康问题有希望.
- 低采用率限制了可用的在线心理健康干预措施的覆盖范围.
研究的目的:
- 开发和评估机器学习模型,用于预测在线干预措施的采用.
- 确定与吸收相关的因素,以告知干预交付.
主要方法:
- 利用了COVID-19大流行期间在线压力干预随机试验的二次数据 (N=301).
- 开发和比较机器学习模型 (包括线性支持矢量机器) 以预测干预采用.
- 包括人口特征,心理健康服务利用率,兴趣和症状作为预测因素.
主要成果:
- 最好的模型 (线性支向量机) 实现了70%的精度和0.70AUC,与其他模型相比.
- 对心理健康治疗的兴趣,以及认定自己是女同性恋,男同性恋,双性恋或其他性少数群体的兴趣,预测了更高的吸收率.
- 男性参与者不太可能实现吸收.
结论:
- 机器学习模型可以在预测在线心理健康干预采用方面实现可接受的性能.
- 自我报告的治疗兴趣是吸收的强有力的预测指标.
- 需要进一步的研究来理解和解决干预采用的性别和性取向差异,并探索针对性参与的机器学习.
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