ArticlePreventive medicine reports2020
Machine learning to identify and understand key factors for provider-patient discussions about smoking.
Article in Preventive medicine reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Assessing the Impact of a Health Education Anti-Smoking Program for Students: A Follow-Up Investigation.Children (Basel, Switzerland) · 2024Article
- Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy.World journal of gastroenterology · 2022Article
- Determinants of Total End-of-Life Health Care Costs of Medicare Beneficiaries: A Quantile Regression Forests Analysis.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data.BMC medical research methodology · 2022Article
- Variable selection with missing data in both covariates and outcomes: Imputation and machine learning.Statistical methods in medical research · 2021Article
- Identify Key Determinants of Contraceptive Use for Sexually Active Young People: A Hybrid Ensemble of Machine Learning Methods.Children (Basel, Switzerland) · 2021Article
- Examining the role of healthcare access in racial/ethnic disparities in receipt of provider-patient discussions about smoking: A latent class analysis.Preventive medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We sought to identify key determinants of the likelihood of provider-patient discussions about smoking and to understand the effects of these determinants. We used data on 3666 self-reported current smokers who talked to a health professional within a year of the time the survey was conducted using the 2017 National Health Interview Survey. We included wide-ranging information on 43 potential covariates across four domains, demographic and socio-economic status, behavior, health status and healthcare utilization. We exploited a principled nonparametric permutation based approach using Bayesian machine learning to identify and rank important determinants of discussions about smoking between health providers and patients. In the order of importance, frequency of doctor office visits, intensity of cigarette use, length of smoking history, chronic obstructive pulmonary disease, emphysema, marital status were major determinants of disparities in provider-patient discussions about smoking. There was a distinct interaction between intensity of cigarette use and length of smoking history. Our analysis may provide some insights into strategies for promoting discussions on smoking and facilitating smoking cessation. Health care resource usage, smoking intensity and duration and smoking-related conditions were key drivers. The "usual suspects", age, gender, race and ethnicity were less important, and gender, in particular, had little effect.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.