Machine learning to identify and understand key factors for provider-patient discussions about smoking.

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, intensi

SUBMITTER: Hu L 

PROVIDER: S-EPMC7666379 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature