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.

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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
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article

Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model

Kaidi Zhang, Jack Yang, Yan Hu, Zhihe Zhao et al.
Nicotine & Tobacco Research
Smoking Behavior and Cessation
article

Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model

Kaidi Zhang, Jack Yang, Yan Hu, Zhihe Zhao, Wenjun Li, Thuy T. T. Le
article en

Abstract

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.

Nicotine & Tobacco Research
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)
Openalex Percentile: Top 49%
Smoking Behavior and Cessation
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Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model — Kaidi Zhang, Jack Yang, et al. · Nicotine & Tobacco Research (2025) | TGRS Research Map | TGRS