Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data.
Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data.
复制标题
DOI：
10.1371/journal.pone.0207523
复制
发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Singleton AD
中科院分区：
文献类型：
--
作者：
Davies A;Green MA;Singleton AD
随着人们对医学的认识不断提高，对小病的自我治疗也越来越多。自我药疗是指一个人“自我”诊断并开处方药进行治疗。自我保健运动具有重要的政策意义，被认为可以减轻国民保健服务（NHS）的负担，增加患者的生存能力，并为更严重的疾病腾出资源。然而，由于缺乏可用数据，很少有研究探讨自我药疗行为在不同人群之间的差异。我们的研究的目的是评估如何高街零售商的忠诚卡数据可以帮助我们了解个人如何在英格兰自我约束。2012-2014年的交易级忠诚卡数据是从英格兰的一家全国性高街零售商处获得的。我们计算了在低超级产出地区购买以下药物的忠诚卡客户（n ~ 1000万）的比例：“咳嗽和感冒”，“Hayfever”，“疼痛缓解”和“太阳制剂”。机器学习被用来探索50个社会人口和健康可及性特征如何与解释每个产品组的购买相关联。随机森林被用作基线，梯度提升作为我们的最终模型。我们的研究结果表明，止痛药是最常见的药物购买。除防晒霜外，性别之间的购买行为几乎没有差异。梯度推进模型表明，地区的社会经济状况以及空气污染是每种药物的重要预测因子。我们的研究通过证明忠诚卡记录对于了解国家层面自我用药如何变化的有用性，增加了自我用药文献。大数据提供了新颖的见解，增加并解决了传统研究无法考虑的问题。通过数据链接获得的新形式的数据可能为改善目前围绕自我药疗行为中的风险人群的公共卫生决策提供机会。
The availability alongside growing awareness of medicine has led to increased self-treatment of minor ailments. Self-medication is where one ‘self’ diagnoses and prescribes over the counter medicines for treatment. The self-care movement has important policy implications, perceived to relieve the National Health Service (NHS) burden, increasing patient subsistence and freeing resources for more serious ailments. However, there has been little research exploring how self-medication behaviours vary between population groups due to a lack of available data. The aim of our study is to evaluate how high street retailer loyalty card data can help inform our understanding of how individuals self-medicate in England. Transaction level loyalty card data was acquired from a national high street retailer for England for 2012–2014. We calculated the proportion of loyalty card customers (n ~ 10 million) within Lower Super Output Areas who purchased the following medicines: ‘coughs and colds’, ‘Hayfever’, ‘pain relief’ and ‘sun preps’. Machine learning was used to explore how 50 sociodemographic and health accessibility features were associated towards explaining purchasing of each product group. Random Forests are used as a baseline and Gradient Boosting as our final model. Our results showed that pain relief was the most common medicine purchased. There was little difference in purchasing behaviours by sex other than for sun preps. The gradient boosting models demonstrated that socioeconomic status of areas, as well as air pollution, were important predictors of each medicine. Our study adds to the self-medication literature through demonstrating the usefulness of loyalty card records for producing insights about how self-medication varies at the national level. Big data offer novel insights that add to and address issues that traditional studies are unable to consider. New forms of data through data linkage may offer opportunities to improve current public health decision making surrounding at risk population groups within self-medication behaviours.
登录
查看更多内容
影响因子：
4.2
作者：
Hughes, CM;McElnay, JC;Fleming, GF
通讯作者：
Fleming, GF
影响因子：
--
作者：
JARRETT, P;SHARP, C;MCLELLAND, J
通讯作者：
MCLELLAND, J
影响因子：
8.7
作者：
Bealey, W. J.;McDonald, A. G.;Fowler, D.
通讯作者：
Fowler, D.
影响因子：
13.6
作者：
Figueiras, A;Caamano, F;Gestal-Otero, JJ
通讯作者：
Gestal-Otero, JJ
影响因子：
3.7
作者：
Gauld NJ;Kelly FS;Emmerton LM;Buetow SA
通讯作者：
Buetow SA