识别戒烟成功的关键预测因素:使用大型语言模型进行基于文本的特征选择
Thuy T T Le1, Jiongxuan Yang2, Zimo Zhao3
1University of Michigan School of Public Health, Department of Health Management and Policy, Ann Arbor, MI, USA.
medRxiv : the preprint server for health sciences
|June 30, 2025
概括
戒烟可以显著降低健康风险. 这项研究使用人工智能识别成功戒烟的关键预测因素,包括吸烟频率和社会影响,以提高干预的有效性.
科学领域:
- 公共卫生 公共卫生
- 行为科学 行为科学
- 人工智能在健康研究中的应用
背景情况:
- 戒烟对于减少死亡率和发病率至关重要.
- 尽管戒烟尝试率很高,但成功戒烟率仍然很低,到2022年,只有10%的人成功戒烟.
研究的目的:
- 为了确定戒烟成功的关键预测因素.
- 为制定更有效的戒烟干预措施提供信息.
- 提高吸烟者成功戒烟率.
主要方法:
- 从人口烟草和健康评估 (PATH) 研究的第五波和第六波数据的分析.
- 利用OpenAI的GPT-4.1来识别基于调查变量描述的12个月禁烟的45个预测变量.
- 使用 eXtreme梯度提升 (XGBoost) 和SHapley添加式扩展 (SHAP) 进行验证的变量选择,用于变量排名.
主要成果:
- 用所有变量和GPT-4.1选定变量训练的XGBoost模型显示了可比性能 (AUC:0.749与0.752).
- 吸烟戒断的主要预测因素包括:30天的吸烟频率,第一次抽烟的时间,社会影响 (重要人的观点,同事的烟草使用),每天使用电子尼古丁产品,情绪依赖和对健康有害的担忧.
结论:
- OpenAI的GPT-4.1有效地识别了与长期戒烟相关的关键变量,仅使用变量描述.
- 这种人工智能驱动的方法可以增强调查设计,提高烟草研究数据收集效率.
- 这些发现可以指导制定有针对性的戒烟策略.
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