Christodoulou Avgi, Katsarou Martha-Spyridoula, Emmanouil Christina, Gavrielatos Marios, Georgiou Dimitrios, Tsolakou Annia, Papasavva Maria, Economou Vasiliki, Nanou Vasiliki, Nikolopoulos Ioannis, Daganou Maria, Argyraki Aikaterini, Stefanidis Evaggelos, Metaxas Gerasimos, Panagiotou Emmanouil, Michalopoulos Ioannis, Drakoulis Nikolaos
Research Group of Clinical Pharmacology and Pharmacogenomics Faculty of Pharmacy, School oh Health Sciences, National and Kapodistrian University of Athens, 15771 Athens, Greece.
Sotiria Thoracic Diseases Hospital of Athens, 11527 Athens, Greece.
BioTech (Basel). 2024 Jul 1;13(3):22. doi: 10.3390/biotech13030022.
Predictive tools provide a unique opportunity to explain the observed differences in outcome between patients of the COVID-19 pandemic. The aim of this study was to associate individual demographic and clinical characteristics with disease severity in COVID-19 patients and to highlight the importance of machine learning (ML) in disease prognosis. The study enrolled 344 unvaccinated patients with confirmed SARS-CoV-2 infection. Data collected by integrating questionnaires and medical records were imported into various classification machine learning algorithms, and the algorithm and the hyperparameters with the greatest predictive ability were selected for use in a disease outcome prediction web tool. Of 111 independent features, age, sex, hypertension, obesity, and cancer comorbidity were found to be associated with severe COVID-19. Our prognostic tool can contribute to a successful therapeutic approach via personalized treatment. Although at the present time vaccination is not considered mandatory, this algorithm could encourage vulnerable groups to be vaccinated.
预测工具为解释新冠疫情患者观察到的结局差异提供了独特机会。本研究的目的是将个体人口统计学和临床特征与新冠患者的疾病严重程度相关联，并强调机器学习（ML）在疾病预后中的重要性。该研究纳入了344例未接种疫苗的确诊SARS-CoV-2感染患者。通过整合问卷和病历收集的数据被导入各种分类机器学习算法，并选择预测能力最强的算法和超参数用于疾病结局预测网络工具。在111个独立特征中，发现年龄、性别、高血压、肥胖和癌症合并症与重症新冠相关。我们的预后工具可通过个性化治疗促成成功的治疗方法。尽管目前疫苗接种不被视为强制性的，但该算法可鼓励弱势群体接种疫苗。
