找到最佳匹配 - - 一个关于数字心理健康干预中 (文本) 特性和模型选择的案例研究
Kirsten Zantvoort1, Jonas Scharfenberger1, Leif Boß2
1Institute of Information Systems, Leuphana University, Lüneburg, Germany.
Journal of healthcare informatics research
|November 6, 2023
概括
预测数字心理健康干预措施中的学率至关重要. 早期用户的文本,特别是深度学习模型和组合功能,显著提高了干预成功的预测准确性.
科学领域:
- 数字心理健康数字心理健康
- 医疗保健中的机器学习
- 自然语言处理自然语言处理.
背景情况:
- 对心理健康服务的需求超过了供应,需要有效的资源配置.
- 预测干预中断是调整治疗方法和改善结果的关键.
- 数字心理健康干预 (DMHI) 提供可扩展的解决方案,但需要方法来识别有风险的个人.
研究的目的:
- 系统地比较文本表示方法和机器学习模型,用于预测DMHIs中断.
- 调查早期用户生成文本和补充数据的预测能力.
- 确定最佳的模型架构来分析心理健康数据中的长文本序列.
主要方法:
- 分析了近16,000个来自849名德语用户的开放文本响应,用于DMHI的压力.
- 系统地比较文本特征:元数据,TF-IDF,情绪/话题分析,词嵌入.
- 评估各种机器学习模型,包括线性模型和深度学习 (基于LSTM的架构).
- 整合社会人口统计,评估和封闭问题数据.
主要成果:
- 与之前的研究相反,神经网络方法对DMHI文本数据显示出重大前景.
- 一个基于特定任务的LSTM模型实现了使用词嵌入用于预测的AUC得分高达0.7.
- 顺序深度学习模型在预测学中表现优于更简单的方法,即使数据有限.
- 来自前两次会议的用户生成文本包含了放弃和失败风险的预测能力.
- 将文本特征与非文本数据相结合,进一步提高了预测准确性.
结论:
- 在DMHIs中的早期用户文本是干预中断和失败的有价值的预测指标.
- 像LSTM这样的先进模型在这个领域对分析长文本序列是有效的.
- 仔细考虑功能复杂性和模型选择对于优化预测结果至关重要.
- 整合不同的数据类型 (文本和非文本) 可以提高整体预测性能.
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