Pinar-Sanchez Juana, Bermejo López Pablo, Solís García Del Pozo Julián, Redondo-Ruiz Jose, Navarro Casado Laura, Andres-Pretel Fernando, Celorrio Bustillo María Luisa, Esparcia Moreno Mercedes, García Ruiz Santiago, Solera Santos Jose Javier, Navarro Bravo Beatriz
Department of Internal Medicine, Jose Maria Morales Meseguer University General Hospital, 30008 Murcia, Spain.
Computer Science Department, Universidad de Castilla-La Mancha, 02071 Albacete, Spain.
J Clin Med. 2022 Apr 6;11(7):2061. doi: 10.3390/jcm11072061.
The diagnosis of alcohol use disorder (AUD) remains a difficult challenge, and some patients may not be adequately diagnosed. This study aims to identify an optimum combination of laboratory markers to detect alcohol consumption, using data science. An analytical observational study was conducted with 337 subjects (253 men and 83 women, with a mean age of 44 years (10.61 Standard Deviation (SD)). The first group included 204 participants being treated in the Addictive Behaviors Unit (ABU) from Albacete (Spain). They met the diagnostic criteria for AUD specified in the Diagnostic and Statistical Manual of mental disorders fifth edition (DSM-5). The second group included 133 blood donors (people with no risk of AUD), recruited by cross-section. All participants were also divided in two groups according to the WHO classification for risk of alcohol consumption in Spain, that is, males drinking more than 28 standard drink units (SDUs) or women drinking more than 17 SDUs. Medical history and laboratory markers were selected from our hospital's database. A correlation between alterations in laboratory markers and the amount of alcohol consumed was established. We then created three predicted models (with logistic regression, classification tree, and Bayesian network) to detect risk of alcohol consumption by using laboratory markers as predictive features. For the execution of the selection of variables and the creation and validation of predictive models, two tools were used: the scikit-learn library for Python, and the Weka application. The logistic regression model provided a maximum AUD prediction accuracy of 85.07%. Secondly, the classification tree provided a lower accuracy of 79.4%, but easier interpretation. Finally, the Naive Bayes network had an accuracy of 87.46%. The combination of several common biochemical markers and the use of data science can enhance detection of AUD, helping to prevent future medical complications derived from AUD.
酒精使用障碍（AUD）的诊断仍然是一项艰巨的挑战，一些患者可能未得到充分诊断。本研究旨在利用数据科学确定用于检测酒精消费的实验室标志物的最佳组合。对337名受试者（253名男性和83名女性，平均年龄44岁（标准差10.61））进行了一项分析性观察研究。第一组包括204名在西班牙阿尔瓦塞特成瘾行为科（ABU）接受治疗的参与者。他们符合《精神疾病诊断与统计手册》第五版（DSM - 5）中规定的AUD诊断标准。第二组包括133名通过横断面招募的献血者（无AUD风险人群）。所有参与者还根据世界卫生组织对西班牙酒精消费风险的分类分为两组，即男性饮酒超过28个标准饮酒单位（SDU）或女性饮酒超过17个SDU。从我院数据库中选取病史和实验室标志物。建立了实验室标志物变化与酒精消费量之间的相关性。然后，我们创建了三个预测模型（逻辑回归、分类树和贝叶斯网络），以实验室标志物作为预测特征来检测酒精消费风险。为了执行变量选择以及预测模型的创建和验证，使用了两种工具：用于Python的scikit - learn库和Weka应用程序。逻辑回归模型的AUD预测准确率最高，为85.07%。其次，分类树的准确率较低，为79.4%，但更易于解释。最后，朴素贝叶斯网络的准确率为87.46%。几种常见生化标志物的组合以及数据科学的应用可以提高AUD的检测率，有助于预防AUD引发的未来医疗并发症。
