A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS

ACS Applied Bio Materials Pub Date : 2024-07-01 DOI:10.3390/biotech13030022

Avgi Christodoulou, Martha-Spyridoula Katsarou, Christina Emmanouil, Marios Gavrielatos, Dimitrios Georgiou, Annia Tsolakou, M. Papasavva, Vasiliki V Economou, Vasiliki Nanou, Ioannis Nikolopoulos, Maria Daganou, A. Argyraki, Evaggelos Stefanidis, Gerasimos Metaxas, E. Panagiotou, Ioannis Michalopoulos, N. Drakoulis

{"title":"A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19","authors":"Avgi Christodoulou, Martha-Spyridoula Katsarou, Christina Emmanouil, Marios Gavrielatos, Dimitrios Georgiou, Annia Tsolakou, M. Papasavva, Vasiliki V Economou, Vasiliki Nanou, Ioannis Nikolopoulos, Maria Daganou, A. Argyraki, Evaggelos Stefanidis, Gerasimos Metaxas, E. Panagiotou, Ioannis Michalopoulos, N. Drakoulis","doi":"10.3390/biotech13030022","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":"138 5","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/biotech13030022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

基于机器学习的 COVID-19 严重性预测网络工具

预测工具为解释所观察到的 COVID-19 大流行患者之间的预后差异提供了一个独特的机会。本研究旨在将 COVID-19 患者的个人人口统计学和临床特征与疾病严重程度联系起来，并强调机器学习 (ML) 在疾病预后中的重要性。该研究招募了 344 名确诊感染 SARS-CoV-2 且未接种疫苗的患者。通过整合问卷调查和医疗记录收集的数据被导入各种分类机器学习算法，然后选出预测能力最强的算法和超参数，用于疾病结果预测网络工具。在 111 个独立特征中，我们发现年龄、性别、高血压、肥胖和癌症合并症与严重 COVID-19 相关。我们的预后工具有助于通过个性化治疗成功地找到治疗方法。虽然目前疫苗接种还不是强制性的，但这种算法可以鼓励弱势群体接种疫苗。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

ACS Applied Bio Materials Chemistry-Chemistry (all)

CiteScore

9.40

自引率

2.10%

发文量

464

期刊介绍： ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.