Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model
Our findings do not establish causal relationships between the selected predictors and 12-month smoking abstinence. However, identifying these key predictors provides valuable insights into the factors highly associated with smoking cessation success. This study demonstrates the ability of OpenAI's GPT-4.1 to perform feature selection using only the textual descriptions of variables. The efficient and successful application of GPT-4.1 for variable selection highlights the potential of integrating artificial intelligence tools into tobacco research to guide resource-efficient and targeted intervention strategies.
Authors
- Kaidi Zhang (ORCID: https://orcid.org/0000-0002-1453-0681)
- Jack Yang (ORCID: https://orcid.org/0000-0003-3589-6807)
- Yan Hu (ORCID: https://orcid.org/0000-0002-7663-3096)
- Zhihe Zhao (ORCID: https://orcid.org/0000-0003-2955-1706)
- Wenjun Li (ORCID: https://orcid.org/0000-0001-5335-7386)
- Thuy T. T. Le (ORCID: https://orcid.org/0000-0002-3106-4045)
Institutions
- University of Massachusetts Lowell (US)
- University of Michigan (US)
- Shenzhen Research Institute of Big Data (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
- Department of Health Services (US)
Publication Details
- Journal
- Nicotine & Tobacco Research
- Published
- 2025-12-18
- DOI
- https://doi.org/10.1093/ntr/ntaf257
- Primary Topic
- Smoking Behavior and Cessation
- Type
- article
- Field-Weighted Citation Impact
- 0.00