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

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Understanding key predictors of smoking cessation success is crucial for effective interventions. This study identified top factors like smoking frequency and social influences using AI, aiding future quit-smoking strategies.

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Area of Science:

  • Tobacco control research
  • Artificial intelligence in health research
  • Behavioral science

Background:

  • Smoking cessation is vital for reducing mortality and morbidity.
  • Despite high quit attempt rates, success rates remain low (around 10%).
  • Identifying predictors of success can enhance intervention efficacy.

Purpose of the Study:

  • To identify key predictors of 12-month smoking abstinence.
  • To evaluate the efficacy of AI (GPT-4.1) in variable selection for smoking cessation research.
  • To derive actionable insights from top predictive variables.

Main Methods:

  • Analysis of Population Assessment of Tobacco and Health (PATH) study data (Waves 5 & 6).
  • Utilized OpenAI's GPT-4.1 for initial selection of 45 predictive variables from textual descriptions.
  • Validated variable importance using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP).

Main Results:

  • XGBoost models showed nearly identical performance whether trained on all variables or GPT-4.1 selected variables (AUC: ~0.75).
  • Top predictors included smoking frequency, time to first cigarette, social influences (peers, important others), and emotional dependence.
  • Concerns about health harms and daily electronic nicotine product use also emerged as significant factors.

Conclusions:

  • GPT-4.1 demonstrated efficient and effective variable selection for smoking cessation predictors.
  • The identified top variables align with known risk factors, offering refined targets for interventions.
  • AI integration in tobacco research can optimize resource allocation for targeted cessation strategies.