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.
概括
了解戒烟成功的关键预测因素对于有效的干预至关重要. 这项研究使用人工智能确定了吸烟频率和社会影响等主要因素,帮助未来的戒烟策略.
科学领域:
- 烟草控制研究 烟草控制研究
- 在健康研究中的人工智能.
- 行为科学是一种行为科学.
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,但成功率仍然很低 (约10%).
- 识别成功的预测因素可以提高干预的有效性.
研究的目的:
- 为了确定12个月禁烟的关键预测因素.
- 评估AI (GPT-4.1) 在戒烟研究的变量选择中的有效性.
- 从顶级预测变量中获得可操作的见解.
主要方法:
- 人口烟草和健康评估 (PATH) 研究数据的分析 (第5波和第6波).
- 利用OpenAI的GPT-4.1从文本描述中初步选择45个预测变量.
- 使用极端梯度提升 (XGBoost) 和夏普利添加式扩展 (SHAP) 验证了变量重要性.
主要成果:
- 无论在所有变量上训练还是在GPT-4.1选定变量上训练,XGBoost模型显示出几乎相同的性能 (AUC:~0.75).
- 最重要的预测因素包括吸烟频率,第一次抽烟的时间,社会影响 (同龄人,重要其他人) 和情绪依赖.
- 对健康危害和日常使用电子尼古丁产品的担忧也成为重要因素.
结论:
- GPT-4.1证明了吸烟戒烟预测因素的高效和有效的变量选择.
- 确定的顶级变量与已知的风险因素保持一致,为干预提供了精细的目标.
- 在烟草研究中整合人工智能可以优化针对性戒烟策略的资源配置.
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