Machine learning to identify and understand key factors for provider-patient discussions about smoking.
Machine learning to identify and understand key factors for provider-patient discussions about smoking.
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DOI：
10.1016/j.pmedr.2020.101238
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发表时间：
2020-12
影响因子：
2.8
通讯作者：
Ji J
中科院分区：
文献类型：
--
作者：
Hu L;Li L;Ji J
在实践中，提供者和患者关于吸烟的讨论并没有被广泛采用。机器学习确定了这种讨论可能性的关键决定因素。关键因素包括医疗资源使用和吸烟强度等。包括年龄、性别和人种/种族在内的人口统计学变量不太重要。我们试图确定关键的决定因素的可能性提供者-患者讨论吸烟，并了解这些决定因素的影响。我们使用了3666名自我报告的当前吸烟者的数据，这些吸烟者在使用2017年全国健康访谈调查进行调查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43个潜在协变量的广泛信息，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。我们利用贝叶斯机器学习的原则性非参数排列方法来识别和排名健康提供者和患者之间关于吸烟的讨论的重要决定因素。按重要性顺序，医生办公室访问的频率，吸烟的强度，吸烟史的长度，慢性阻塞性肺疾病，肺气肿，婚姻状况是主要的决定因素，在提供者与患者讨论吸烟的差异。有一个明显的相互作用之间的强度的香烟使用和吸烟史的长度。我们的分析可能会提供一些见解的策略，促进吸烟和促进戒烟的讨论。卫生保健资源使用、吸烟强度和持续时间以及吸烟相关条件是关键驱动因素。“通常的嫌疑人”，年龄，性别，种族和民族是不太重要的，性别，特别是几乎没有影响。
Provider-patient discussions about smoking have not been widely adopted in practice. Machine learning identified key determinants of the likelihood of such discussions. Key factors included healthcare resource usage and smoking intensity, among others. Demographic variables including age, gender and race/ethnicity were less important. 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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影响因子：
1.8
作者：
Bleich, Justin;Kapelner, Adam;Jensen, Shane T.
通讯作者：
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影响因子：
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DOI：
10.1080/19345747.2011.618213
发表时间：
2012-01-01
影响因子：
1.8
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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