BACKGROUND Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' detailed smoking status. Researchers have successfully extracted smoking history from electronic health records (EHRs) using text mining methods. However, they could not retrieve patients' smoking intensity due to its limited availability in the EHR. The presence of detailed smoking information in the electronic dental record (EDR) often under a separate section allows retrieving this information with less preprocessing. OBJECTIVE To determine patients' detailed smoking status based on smoking intensity from the EDR. METHODS First, the authors created a reference standard of 3,296 unique patients' smoking histories from the EDR that classified patients based on their smoking intensity. Next, they trained three machine learning classifiers (support vector machine, random forest, and naïve Bayes) using the training set (2,176) and evaluated performances on test set (1,120) using precision (P), recall (R), and F-measure (F). Finally, they applied the best classifier to classify smoking status from an additional 3,114 patients' smoking histories. RESULTS Support vector machine performed best to classify patients into smokers, nonsmokers, and unknowns (P, R, F: 98%); intermittent smoker (P: 95%, R: 98%, F: 96%); past smoker (P, R, F: 89%); light smoker (P, R, F: 87%); smokers with unknown intensity (P: 76%, R: 86%, F: 81%), and intermediate smoker (P: 90%, R: 88%, F: 89%). It performed moderately to differentiate heavy smokers (P: 90%, R: 44%, F: 60%). EDR could be a valuable source for obtaining patients' detailed smoking information. CONCLUSION EDR data could serve as a valuable source for obtaining patients' detailed smoking information based on their smoking intensity that may not be readily available in the EHR.

中文翻译：

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利用电子牙科记录数据根据患者的吸烟强度对患者进行分类。

背景技术吸烟是确定的口腔疾病危险因素，因此，牙科临床医生常规评估并记录患者的详细吸烟状况。研究人员已使用文本挖掘方法成功地从电子健康记录（EHR）中提取了吸烟史。但是，由于EHR中的可用性有限，他们无法恢复患者的吸烟强度。电子牙齿记录（EDR）中通常在单独的部分下包含详细的吸烟信息，因此可以通过较少的预处理来获取该信息。目的根据EDR的吸烟强度确定患者的详细吸烟状况。方法首先，作者根据EDR建立了3,296名独特患者吸烟史的参考标准，该标准对患者的吸烟强度进行了分类。接下来，他们使用训练集（2,176）训练了三个机器学习分类器（支持向量机，随机森林和朴素贝叶斯），并使用精度（P），召回率（R）和F-对测试集（1,120）的性能进行了评估测量（F）。最后，他们使用最佳分类器从另外3114位患者的吸烟史中对吸烟状况进行分类。结果支持向量机在将患者分类为吸烟者，非吸烟者和未知患者方面表现最佳（P，R，F：98％）；间歇吸烟者（P：95％，R：98％，F：96％）; 过去吸烟者（P，R，F：89％）；轻度吸烟者（P，R，F：87％）；强度未知的吸烟者（P：76％，R：86％，F：81％）和中度吸烟者（P：90％，R：88％，F：89％）。它的表现中等，可以区分重度吸烟者（P：90％，R：44％，F：60％）。EDR可能是获取患者病情的宝贵来源 详细的吸烟信息。结论EDR数据可作为根据患者的吸烟强度获得患者详细吸烟信息的宝贵来源，而EHR可能无法轻易获得这些信息。