BIO-XRNET:一种强大的多模式叠加机器学习技术,用于使用胸部X射线图像和临床数据预测新冠肺炎患者的死亡率风险。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tawsifur Rahman, Muhammad E H Chowdhury, Amith Khandakar, Zaid Bin Mahbub, Md Sakib Abrar Hossain, Abraham Alhatou, Eynas Abdalla, Sreekumar Muthiyal, Khandaker Farzana Islam, Saad Bin Abul Kashem, Muhammad Salman Khan, Susu M Zughaier, Maqsud Hossain
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引用次数: 4

摘要

如今,对新冠肺炎进行快速、准确的诊断是迫切需要。本研究提出了一种多模式系统来满足这一需求。所提出的系统采用机器学习模块,该模块从新冠肺炎第一波疫情期间(2020年3月至6月)在意大利住院的930名新冠肺炎患者收集的数据集中学习所需知识。该数据集由来自电子健康记录和胸部X射线(CXR)图像的25个生物标志物组成。研究发现,该系统可以诊断低风险或高风险患者,准确率、灵敏度和F1评分分别为89.03%、90.44%和89.03%。该系统表现出比使用CXR图像或生物标志物数据的系统高6%的准确性。此外,该系统还可以使用基于多变量逻辑回归的列线图评分技术计算高危患者的死亡风险。感兴趣的医生可以使用所提供的系统,使用网络链接:COVID-severity-grading-AI来预测新冠肺炎患者的早期死亡率风险。在这种情况下,医生需要输入以下信息:CXR图像文件、乳酸脱氢酶(LDH)、血氧饱和度(O2%)、白细胞计数、C反应蛋白和年龄。通过这种方式,这项研究通过预测早期死亡风险,为新冠肺炎患者的管理做出了贡献。补充信息:在线版本包含补充材料,可访问10.1007/s00521-023-08606-w。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.

Supplementary information: The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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