Hirvonen Eveliina, Karlsson Antti, Saaresranta Tarja, Laitinen Tarja
Division of Medicine, Department of Pulmonary Diseases, Turku University Hospital, Turku, Finland.
Department of Pulmonary Diseases and Clinical Allergology, University of Turku Turku Finland.
Eur Clin Respir J. 2021 Nov 23;8(1):2004664. doi: 10.1080/20018525.2021.2004664. eCollection 2021.
Smoking cessation is essential part of a successful treatment in many chronic diseases. Our aim was to analyse how actively clinicians discuss and document patients' smoking status into electronic health records (EHR) and deliver smoking cessation assistance.
We analysed the results using a combination of rule and deep learning-based algorithms. Narrative reports of all adult patients, whose treatment started between years 2010 and 2016 for one of seven common chronic diseases, were followed for two years. Smoking related sentences were first extracted with a rule-based algorithm. Subsequently, pre-trained ULMFiT-based algorithm classified each patient's smoking status as a current smoker, ex-smoker, or never smoker. A rule-based algorithm was then again used to analyse the physician-patient discussions on smoking cessation among current smokers.
A total of 35,650 patients were studied. Of all patients, 60% were found to have a smoking status in EHR and the documentation improved over time. Smoking status was documented more actively among COPD (86%) and sleep apnoea (83%) patients compared to patients with asthma, type 1&2 diabetes, cerebral infarction and ischemic heart disease (range 44-61%). Of the current smokers (N=7,105), 49% had discussed smoking cessation with their physician. The performance of ULMFiT-based classifier was good with F-scores 79-92.
Ee found that smoking status was documented in 60% of patients with chronic disease and that the clinician had discussed smoking cessation in 49% of patients who were current smokers. ULMFiT-based classifier showed good/excellent performance and allowed us to efficiently study a large number of patients' medical narratives.
戒烟是许多慢性病成功治疗的重要组成部分。我们的目的是分析临床医生在电子健康记录（EHR）中讨论和记录患者吸烟状况以及提供戒烟帮助的积极性如何。
我们结合基于规则和深度学习的算法来分析结果。对2010年至2016年期间开始治疗七种常见慢性病之一的所有成年患者的叙述性报告进行了为期两年的跟踪。首先使用基于规则的算法提取与吸烟相关的句子。随后，基于预训练的ULMFiT算法将每个患者的吸烟状况分类为当前吸烟者、既往吸烟者或从不吸烟者。然后再次使用基于规则的算法来分析当前吸烟者中医生与患者关于戒烟的讨论。
共研究了35650名患者。在所有患者中，发现60%在EHR中有吸烟状况记录，且记录情况随时间有所改善。与哮喘、1型和2型糖尿病、脑梗死和缺血性心脏病患者（范围为44%-61%）相比，慢性阻塞性肺疾病（COPD）患者（86%）和睡眠呼吸暂停患者（83%）的吸烟状况记录更为积极。在当前吸烟者（N=7105）中，49%与他们的医生讨论过戒烟。基于ULMFiT的分类器表现良好，F值为79-92。
我们发现60%的慢性病患者有吸烟状况记录，且49%的当前吸烟者的临床医生讨论过戒烟。基于ULMFiT的分类器表现良好/出色，使我们能够有效地研究大量患者的医疗叙述。
