使用机器学习从人类行为改变项目的戒烟干预措施随机试验报告中提取信息并预测结果
Robert West1, Francesca Bonin2, James Thomas3
1Research Department of Behavioural Science and Health, University College London, London, England, UK.
Wellcome open research
|May 23, 2024
概括
机器学习 (ML) 显示出从戒烟试验报告中提取数据的希望,但面临挑战. 需要改进报告标准和新的算法来更好地预测结果.
科学领域:
- 健康行为研究研究健康行为研究
- 计算语言学计算语言学
- 机器学习在医学中的应用.
背景情况:
- 人类行为变化项目旨在开发和评估机器学习 (ML) 算法,用于从研究报告中提取信息和预测结果.
- 这项研究使用了戒烟干预的随机试验作为开发这些ML算法的测试案例.
研究的目的:
- 开发和评估ML算法,从戒烟试验报告中提取关键信息.
- 根据提取的研究数据,评估ML算法在预测戒烟结果方面的能力.
主要方法:
- 在70个信息实体的512个戒烟试验报告中使用行为变化干预实体学 (Behaviour Change Intervention Ontology) 手动注释.
- 训练使用FLAIR框架进行自动信息提取的命名实体识别 (NER) 系统.
- 开发一种深度学习算法,使用长期短期记忆 (LSTM) 组件来预测戒烟的结果.
主要成果:
- 信息提取ML算法平均F1得分为0.42,远低于人类注释器平均得分0.75.
- 分配实体进行研究的算法 (干预与控制) 没有成功.
- 最初的ML结果预测算法没有超过简单的预测方法,如平均结果值或线性回归.
结论:
- 虽然ML在从试验报告中提取信息方面取得了一些成功,但仍然存在重大挑战.
- 报告随机对照试验的更大标准化对于改进ML应用至关重要.
- 需要新的ML算法,可能包含本体信息,以提高戒烟研究的结果预测.
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