Suppr超能文献

机器学习用于识别和理解医患关于吸烟问题讨论的关键因素。

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

作者信息

Hu Liangyuan, Li Lihua, Ji Jiayi

机构信息

Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

The Institute for Healthcare Delivery, Mount Sinai Health System, New York, NY, USA.

出版信息

Prev Med Rep. 2020 Nov 5;20:101238. doi: 10.1016/j.pmedr.2020.101238. eCollection 2020 Dec.

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.

摘要

我们试图确定医患讨论吸烟可能性的关键决定因素,并了解这些决定因素的影响。我们使用了2017年国家健康访谈调查的数据,这些数据来自3666名自我报告的当前吸烟者,他们在调查进行的一年内与健康专业人员进行了交谈。我们纳入了四个领域中43个潜在协变量的广泛信息,包括人口统计学和社会经济状况、行为、健康状况和医疗保健利用情况。我们采用了一种基于贝叶斯机器学习的有原则的非参数排列方法,以识别和排列医疗服务提供者与患者之间关于吸烟讨论的重要决定因素。按重要性排序,医生门诊就诊频率、吸烟强度、吸烟史长度、慢性阻塞性肺疾病、肺气肿、婚姻状况是医患关于吸烟讨论差异的主要决定因素。吸烟强度与吸烟史长度之间存在明显的相互作用。我们的分析可能为促进吸烟讨论和推动戒烟的策略提供一些见解。医疗保健资源使用、吸烟强度和持续时间以及与吸烟相关的状况是关键驱动因素。常见的因素,如年龄、性别、种族和民族,重要性较低,尤其是性别,影响很小。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f85f/7666379/16eb7a51d76c/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验